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Hannah Nelson


Clinician engagement in the EHR implementation planning process may help optimize health IT to meet end-user needs.


Proactive leadership, clinician engagement, and workflow-specific training are key to successful EHR implementations, according to a study published in JAMIA.

Selecting a new, presumably better EHR can help healthcare organizations keep up with evolving EHR-related regulations and mitigate clinician burden, the authors noted. However, ensuring the implementation is well-planned and informed by clinicians is necessary for success, they said. 

PROACTIVE LEADERSHIP

The study authors emphasized that organizational leaders must create a governance structure that includes experts with operational and technical expertise to oversee the EHR transition and intervene as issues arise.

Organizational leaders should also ensure that clinicians are engaged in implementation planning and EHR configuration, they said.

“Clinicians are used to their daily workflows based on specific screens and content within the user interface of their existing EHR, but many of these workflow processes will be disrupted by the new EHR user interface displays, content, and navigation pathways as well as variations in adoption rates for these new workflows by the larger care team,” the authors explained.

Governance structures that ensure clinician engagement in the EHR implementation planning process could help optimize the EHR to meet user needs, the authors said.

PROACTIVE RISK ASSESSMENT

The study authors noted that EHR implementations often result in workarounds that bypass safety procedures. For instance, an organization may need to modify existing clinical decision support mechanisms for a new EHR implementation.

“Proactive risk assessment, or the identification of potential risks before they occur with a goal of either mitigating their impact or preventing them from occurring, is thus essential,” the authors said.

The authors emphasized that the best strategy for proactive risk assessment is to useONC’s SAFER (Safety Assurance Factors for EHR Resilience) Guides, a suite of checklists that identify safety concerns related to EHR technology and clinical processes. 

REDUCE UNWARRANTED VARIATION

Next, the study authors said that healthcare organizations should standardize certain EHR features, functions, and workflows to reduce unwarranted variation within the system.

For example, some healthcare organizations allow clinicians to create personalized order sets. However, this introduces significant practice variation and introduces long-term maintenance issues, the writers pointed out.

“Organizations should anticipate similar unwarranted variations related to EHR design, development, configuration, and use,” they wrote. “These variations worsen quality and increase cost.”

Additionally, reducing unwarranted variation in how clinical data is defined, coded, and stored can help increase interoperability with EHRs from other healthcare organizations, they explained.

FORESEE INFORMATION ACCESS PROBLEMS

Ensuring providers maintain access to complete patient health data is essential to providing uninterrupted care during EHR implementations.

The study authors said that healthcare organizations should import as much valid coded data and free text data from their old EHR as possible in anticipation of information access problems.

“It may not be possible to automate all data migration tasks, therefore, healthcare organizations should invest in resources to manually migrate or ‘curate and prep’ each patient’s chart to reduce the cognitive burden associated with data reconciliation for each patient’s first visit with the new EHR,” they wrote.

Healthcare organizations should also review and test their data migrations to ensure they are complete, correct, and commensurate with newly collected data. If all necessary data cannot be migrated to the new system, read-only access to the old EHR needs to be maintained.

SUPPORT END-USERS

In preparation of go-live, healthcare organizations should provide end-users with “protected time” away from clinical duties to attend workflow specific training, the study authors noted.

Healthcare organizations should also provide additional one-on-one support to clinicians who continue to struggle with the system, they suggested.

In addition, healthcare organizations leadership should reduce the number of patients seen in clinics or treated in the hospital for the first few weeks following go-live while the system’s inevitable “kinks” are worked out, the authors wrote.






Hannah Nelson


Dell Medical School at UT is piloting an ONC-funded health IT implementation that aims to support interoperability for social services referrals. 


Dell Medical School at the University of Texas (UT) is piloting health IT that aims to enhance social services referral interoperability between clinics and service providers according to reporting from The Austin American Statesman.

The health IT seeks to help healthcare providers to make social services referrals within their EHR workflow. The agency can then notify the provider that the patient has been connected and is receiving services, or that it was unable to help the patient.

Austin nonprofit Integrated Care Collaboration is helping create the interoperability tool. People's Community Clinic, a federally qualified health center (FQHC) in Austin, and Integral Care, the mental health authority of Travis County, will pilot the technology at the beginning of 2022.

In 2023, Dell Medical School will roll out the health IT in El Paso and New Orleans to ensure it has a universal application rather than just working in Central Texas.

Anjum Khurshid, MD, PhD, associate professor and director of data integration in the department of population health at Dell Medical School, is leading the interoperability work.

"We're very excited to give our community a chance to address the needs of the underserved community," Khurshid told the news outlet. "We have to build this for those who need it the most: the underrepresented.”

On the patient side, Dell Medical School is building an app that will allow patients to log into all their patient portals in one place, officials said. This tool will allow patients to view all of their patient portal information, including social services referrals.

Officials noted that researchers are designing the health IT to minimize clinician burden by eliminating the need to fill out the same information on multiple forms repeatedly. The clinic will be able to send all of the patient's information to multiple social services providers.  

Khurshid said that ultimately, researchers want the tool to be scalable and standardized to any community that wants it.

The ONC funded the project with a $998,118 grant through its 2021 Leading Edge Acceleration Projects (LEAP) in Health IT program.






Erin McNemar


Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health.


Population health management has become an important method for improving community health.  

As the population health management market continues to develop in the healthcare space, systems must gather data from multiple sources, apply analytics to the data, and manage the care for the population. 

The health management method relies on data analytics to identify populations in need of care, measure the care provided to those populations, and deliver care to the correct people.  

The process of population health management begins by gathering key demographic and clinical data about patients, often from electronic health records.  

Through data analytics and population health management, providers can improve patient outcomes, enhance care management, and address social determinants of health.  

USING DATA TO IMPROVE PATIENT OUTCOMES 

To best serve a group of individuals, providers and physicians must utilize data. Big data is often used to address population health concerns to assist large communities of people.  

In a panel covered by HealthITAnalytics, Jefferson Health’s medical information officer Bracken Babula explained how understanding patient metrics and risk scores development is critical to the data collection process.  

“Some of the things that we’re trying to start figuring out how to use are risk scores that might pull a number of different metrics from all over the system. Basics like age, gender, insurance, and more complicated things like certain past medical history and lab values. We can then pull that all into a broader overview of the patient, with the idea being that you can then target your outreach,” Babula said. 

Through data analytics, medical professionals can gain insights into patient needs and allocate resources to those who may need them more, improving care management. 

ENHANCING CARE MANAGEMENT 

By implementing population health management strategies and data analytics, providers are replacing the “one size fits all” care mentality to deliver value-based care

The purpose of value-based care is to standardize the healthcare process by enhancing the patient experience, the health of patient populations, and the cost of care. Through data analytics, providers can assess which processes are the most effective methods for wellness and prevention within value-based care models.   

According to Cleveland Clinic, “Prevention of health (through quitting smoking, dietary and lifestyle changes, exercise, etc.) reduces the need for expensive tests, procedures, and medications. You’re staying well cuts healthcare costs for everyone.”  

With population health management, organizations can consider physical and social determinants of health that may impact individuals and focus on “well care” rather than waiting for a patient to become ill.   

ADDRESSING SOCIAL DETERMINANTS OF HEALTH NEEDS 

Increasingly, data analytics and population health management are being used in work with social determinants of health. At Stanford Children’s, researchers are collecting data from patients to better understand environmental factors that could influence an individual’s health. 

“The one huge aspect of this that we’re looking at Stanford Children’s is around the social determinants of health. Understanding what are the conditions, beyond just the typical things you collect in a physician visit. Is there domestic violence or food insecurities, or things like that, that really would ultimately affect the patient’s health down the road and may have different interventions than a typical physician visit?” revealed Stanford Children’s chief analytics officer Brendan Watkins. 

Jefferson Hospital also studied social determinants of health regarding the COVID-19 vaccine. The hospital used metrics called the social vulnerability index and the community need index to assess and target where the vaccines should go. 

THE FUTURE OF BIG DATA IN POPULATION HEALTH MANAGEMENT 

As data analytics continues to grow in the population health management space, Geisinger director of machine learning Abdul Tariq told HealthITAnalytics that she envisions consumer health informatics expanding not only in healthcare but also in the tech and provider world. 

“As more and more people get these wearable devices like Fitbit, Apple Watch, that data will start getting captured, then there will be a market that will open up where technology companies will start providing some of these insights that traditional health systems have provided,” Tariq said. 

“With that regulation, I’m sure there will be policy enactments that will change how providers deliver care. Then, eventually, the policy will shift how providers, systems, get into this space, and what that means.” 

With wearable devices, there is an opportunity for providers to access that data to improve patient outcomes. Through data analytics and population health management, systems can identify populations in need, stratify risk, and track patient progress.  



Hannah Nelson


An updated health IT roadmap outlines several initiatives that aim to boost interoperability across the state for improved care coordination.


 Colorado’s Office of eHealth Innovation (OeHI) has released an updated health IT roadmap which outlines the state’s data sharing approach that aims to enhance interoperability and improve care coordination.

The roadmap, which was a collaborative effort between over 50 individuals and organizations across the state, outlined the state’s overarching interoperability goals: supporting better data sharing infrastructure, increasing access to in-person and virtual care through coordinated systems, and improving health equity.

While most hospitals in the state are connected to health information exchanges (HIEs), many healthcare organizations, in particular rural safety net and behavioral health providers, are not able to share information broadly through HIEs due to affordability and outdated EHR systems.  

Colorado’s Rural Connectivity Program, led by OeHI and the eHealth Commission, is focused on helping rural safety net and behavioral health providers to connect to HIEs.

“Opening equitable, secure, and affordable IT pathways for patients, their providers, payers, community partners, and state agencies to connect with and share health and social services, information, and data would create an IT ecosystem that provides a more holistic look at health,” OeHI officials noted in the roadmap.

“Addressing this need puts Colorado stakeholders in prime position for advancing Colorado’s goals of affordability, access, and equity,” they continued.

Greater patient data sharing across providers is expected to reduce duplicative services and lower costs, the roadmap authors pointed out. Supporting better data sharing infrastructure across clinical and social care organizations is also expected to improve care access by focusing on whole-person health, the roadmap noted.

OeHI said that many health systems, communities, and state agencies are actively working on sharing information to improve whole-person care.

For instance, the Colorado Department of Human Services and the Colorado Department of Healthcare Policy & Financing’s joint agency interoperability efforts will enable cross-agency information sharing through data standardization, officials said.

Additionally, the Office of Behavioral Health will implement a plan to strengthen the behavioral health safety net system through coordinated health IT infrastructure, OeHI officials wrote.

“This step is critical for the Behavioral Health Administration, which will align, coordinate, and integrate state mental health and substance use programs and funding to streamline access and lower barriers to services for patients,” they said.

Next, OeHI said it will strive to provide all Coloradans with access to high-quality in-person, virtual, and remote health services that are coordinated through health IT systems.

The COVID-19 virtual health boom solidified telehealth as a convenient and reliable solution for delivering whole-person care, officials noted.

Providing whole-person care requires information from virtual and remote visits to be available, accessible, and shared across the care continuum. However, EHR systems do not always incorporate virtual and remote tools readily. Additionally, some virtual providers, especially those delivering care to Coloradans out of state, are not sharing data through health information exchanges.

The roadmap outlines Colorado’s plan for a Social-Health Information Exchange (S-HIE) infrastructure. The person-centered network is set to include a robust statewide resource directory, interoperable platforms for referral and care coordination, and functionality to track health outcomes.

“OeHI and the eHealth Commission are committed to the development of a flexible and interoperable S-HIE infrastructure that supports coordinated whole-person care across the physical, social, and behavioral health domains,” the roadmap authors wrote.

“Whole-person care coordination across in-person, virtual, and remote services for personal health and social needs is only possible with a connected and interoperable ecosystem and infrastructure,” the authors noted.

Lastly, the roadmap outlined the goal of improving health equity through the inclusive and innovative use of health IT and digital health tools.

“Several studies have found that non or limited English speakers have lower rates of telemedicine use, strengthening the need to consider how systems can better integrate live interpretation and digital translation services into their infrastructure to promote greater communication and digital equity,” the authors wrote.






Hannah Nelson


An updated health IT roadmap outlines several initiatives that aim to boost interoperability across the state for improved care coordination.


Colorado’s Office of eHealth Innovation (OeHI) has released an updated health IT roadmap which outlines the state’s data sharing approach that aims to enhance interoperability and improve care coordination.

The roadmap, which was a collaborative effort between over 50 individuals and organizations across the state, outlined the state’s overarching interoperability goals: supporting better data sharing infrastructure, increasing access to in-person and virtual care through coordinated systems, and improving health equity.

While most hospitals in the state are connected to health information exchanges (HIEs), many healthcare organizations, in particular rural safety net and behavioral health providers, are not able to share information broadly through HIEs due to affordability and outdated EHR systems.  

Colorado’s Rural Connectivity Program, led by OeHI and the eHealth Commission, is focused on helping rural safety net and behavioral health providers to connect to HIEs.

“Opening equitable, secure, and affordable IT pathways for patients, their providers, payers, community partners, and state agencies to connect with and share health and social services, information, and data would create an IT ecosystem that provides a more holistic look at health,” OeHI officials noted in the roadmap.

“Addressing this need puts Colorado stakeholders in prime position for advancing Colorado’s goals of affordability, access, and equity,” they continued.

Greater patient data sharing across providers is expected to reduce duplicative services and lower costs, the roadmap authors pointed out. Supporting better data sharing infrastructure across clinical and social care organizations is also expected to improve care access by focusing on whole-person health, the roadmap noted.

OeHI said that many health systems, communities, and state agencies are actively working on sharing information to improve whole-person care.

For instance, the Colorado Department of Human Services and the Colorado Department of Healthcare Policy & Financing’s joint agency interoperability efforts will enable cross-agency information sharing through data standardization, officials said.

Additionally, the Office of Behavioral Health will implement a plan to strengthen the behavioral health safety net system through coordinated health IT infrastructure, OeHI officials wrote.

“This step is critical for the Behavioral Health Administration, which will align, coordinate, and integrate state mental health and substance use programs and funding to streamline access and lower barriers to services for patients,” they said.

Next, OeHI said it will strive to provide all Coloradans with access to high-quality in-person, virtual, and remote health services that are coordinated through health IT systems.

The COVID-19 virtual health boom solidified telehealth as a convenient and reliable solution for delivering whole-person care, officials noted.

Providing whole-person care requires information from virtual and remote visits to be available, accessible, and shared across the care continuum. However, EHR systems do not always incorporate virtual and remote tools readily. Additionally, some virtual providers, especially those delivering care to Coloradans out of state, are not sharing data through health information exchanges.

The roadmap outlines Colorado’s plan for a Social-Health Information Exchange (S-HIE) infrastructure. The person-centered network is set to include a robust statewide resource directory, interoperable platforms for referral and care coordination, and functionality to track health outcomes.

“OeHI and the eHealth Commission are committed to the development of a flexible and interoperable S-HIE infrastructure that supports coordinated whole-person care across the physical, social, and behavioral health domains,” the roadmap authors wrote.

“Whole-person care coordination across in-person, virtual, and remote services for personal health and social needs is only possible with a connected and interoperable ecosystem and infrastructure,” the authors noted.

Lastly, the roadmap outlined the goal of improving health equity through the inclusive and innovative use of health IT and digital health tools.

“Several studies have found that non or limited English speakers have lower rates of telemedicine use, strengthening the need to consider how systems can better integrate live interpretation and digital translation services into their infrastructure to promote greater communication and digital equity,” the authors wrote.






Erin McNemar


With data analytics, researchers found that some populations are underrepresented in cancer clinical trials, which contributes to health disparities.


According to a data analytics study examining health disparities in cancer clinical trials, certain populations remain underrepresented despite attempts to increase participation.

By including individuals with diverse backgrounds in clinical trials, researchers can determine if treatments are safe and effective for people with different characteristics, improving population health. To improve diversity in clinical trials, the National Cancer Institute (NCI) has created several initiatives.

Juan F. Javier-DesLoges, MD, MS, of UC San Diego Health, and his colleagues analyzed the NCI Clinical Data Update System, a database that contains records on participants in NCI-sponsored clinical trials, to examine the representation of minorities, women, and older patients in 766 breast, colorectal, lung, and prostate cancer trials from 2000–2019.

The trials in the data analysis included 242,720 participants: 197,320 Non-Hispanic White (81.3 percent), 21,190 Black (8.7 percent), 11,587 Hispanic (4.8 percent), and 6,880 Asian/Pacific Islander (2.8 percent) patients.

The research team analyzed clinical trial participation from 2015- 2019 compared to the proportion of cancer incidence rates from 2015- 2017 for non-Hispanic Whites versus minorities, elderly versus nonelderly patients, and female versus male patients.

According to the results, Black and Hispanic patients were more likely to participate in breast cancer clinical trials but were significantly underrepresented in colorectal, lung, and prostate cancer trials. Additionally, patients over 65 were underrepresented in breast, colorectal, and lung cancer trials while women were underrepresented in colorectal and lung cancer trials.

When the team examined 2000–2004 and 2015–2019, they discovered that Hispanic and Black patients were more likely to be included in breast, lung, and prostate cancer trials in recent years than in the early 2000s.

For women, they were less likely to be included in colorectal cancer trials in recent years. However, women were more likely to participate in lung cancer trials. Trends in inclusion for patients older than 65 years varied depending on the cancer type.

“Our article indicates that the disparity for clinical enrollment in NCI clinical trials has narrowed for minorities, but further efforts are still needed,” Javier-DesLoges said in a press release.

According to researchers, additional work needs to be conducted to address the ongoing underrepresentation of women and older patients in clinical trials.

To eliminate health disparities, researchers must identify the root cause of the problem and development methods to promote health equality. By improving the representation in clinical trials, researchers can enhance population health and provide better patient outcomes.

While representation in clinical trials has increased, many populations remain underrepresented.





Hannah Nelson


The number of APIs that integrate with certified EHR technology is expected to continue to climb as more developers meet Cures Act requirements.


ONC research has found an increase in the number of application programming interface (API) adoption that integrate with certified EHR technology, which should increase patient access to health information, according to a HealthITBuzz blog post written by ONC’s Christian Johnson and Vaishali Patel.

The ONC 21st Century Cures Act Final Rule, published in 2020, built upon previous federal initiatives to enhance patient access to personal health information through standards-based API adoption.

APIs make it easier patients to use smartphones, tablets, and desktop apps to access their personal health information from certified EHR systems.

ONC survey data has revealed a rapid uptick in healthcare providers enabling patient data access through APIs. In 2019, about 7 in 10 non-federal acute care hospitals enabled this capability, which is a two-fold increase compared to 2017.

New ONC research also found an increase in APIs that integrate with certified EHRs. Johnson and Patel said they expect this upward trend to continue as more certified health IT developers meet requirements of the Cures Act Final Rule.

“As the number of apps that connect with EHRs increases and the functionality of these apps becomes more robust, we believe this will increase demand and use by patients,” Johnson and Patel explained.

ONC and the greater health IT community are working to make patient health information available in the most convenient, user-friendly format, Johnson and Patel said.

Access to personal health information is expected to encourage patient engagement, which could improve patient outcomes.

Previous ONC research revealed that one of the most common reasons patients don’t access their electronic personal health information was because they did not feel like they had a medical reason to do so. However, Johnson and Patel noted that patients should shift their perspective to think more proactively when it comes to accessing their EHR data.

“While healthy individuals may think they currently do not have a medical need to electronically access their health records, it is impossible to predict when a medical emergency – and the need for these records – may arise; the convenience to do so is a substantial benefit,” they wrote.

Johnson and Patel said that patient education about the benefits of electronic access to health records could help increase patient portal and health app use.  

“Healthcare providers having conversations with their patients and encouraging them to use these tools has been shown to increase uptake,” they noted.

When the Cures Act Final Rule API provisions go into effect on December 31, 2022, patients should be more readily able to access and manage their health information, Johnson and Patel said.

Equally as important as patients’ ability to electronically access their health information is their understanding of keeping their information secure, the pair added.

Johnson and Patel explained that while HIPAA provides for the privacy of health information, as well as the right of access, the decision to access, use, or share one’s personal electronic health information is up to the individual. 

“ONC continues to take steps to protect health information, including a number of security-related capabilities in the 2015 Edition Standards and Certification criteria, and we encourage the development of digital health products that demonstrably protect patient health information,” Johnson and Patel concluded.





Hannah Nelson


An expert panel pointed out interoperability gaps and other faults in public health data systems that led to poor COVID-19 emergency response.


ONC’s Health Information Technology Advisory Committee (HITAC) recently held an expert panel hearing to investigate interoperability and public health data system performance during COVID-19.

In an ONC blogpost, Aaron Miri, MBA, FCHIME, FHIMSS, CHCIO and Denise Webb, MA, outlined several key takeaways from the hearing.

Dan Jernigan, MD, MPH, CDC deputy director for public health science and surveillance, gave opening remarks that highlighted the CDC’s sustainable approach to improve public health data systems in the long term, not just for pandemic relief efforts.

Tom Frieden, MD, MPH, president and CEO of Resolve to Save Lives and former CDC director, stated that an effective public health emergency response involves learning, adjusting, and adapting as the industry moves along.

In preparation for future public health disasters, healthcare professionals on every level will need to work together, Frieden explained. He noted pre-existing weaknesses of the public health ecosystem, including interoperability gaps between federal, state, and local public health departments.

To close these interoperability gaps, the industry must build systems that can be ramped up to seamlessly meet the needs of a public health emergency, Mark McClellan, MD, PhD, of the Duke Robert J. Margolis Center for Health Policy said.

“Our data systems have not been consistently connected or integrated across public health and healthcare,” he explained.

Linda Rae Murray, MD, MPH, an assistant professor at the University of Illinois School of Public Health, emphasized that policy and organizational roadblocks to seamless patient data exchange contribute to health inequities.

Information from economic systems, political systems, and faith-based systems could be integrated into public health data systems to give a more complete picture of individual and population level health, she noted.

“Issues we face around data systems are political science problems, not computer science problems,” added Michael Fraser, PhD, CEO of the Association of State and Territorial Health Officials.

Panelists also called on HITAC to promote partnerships across all sectors. President Biden’s recent Executive Order on Ensuring a Data-Driven Response to COVID-19 and Future High-Consequence Public Health Threats emphasizes on the importance of collecting and sharing COVID-19 data with state, local, tribal, and territorial authorities.

According to Karen DeSalvo, MD, MPH, MSc, chief health officer for Google Health and former national coordinator for health IT, addressing interoperability deficiencies requires a 21st century public health infrastructure. She emphasized the importance of open standards, not proprietary ones.

Panelists pointed out specific concerns regarding how outdated technology practices lead to interoperability issues during COVID-19.

In particular, Joneigh Khaldun, MD, brought up the issue of widespread spreadsheet use for information gathering at the height of the pandemic. In Michigan, data processing capabilities were “outdated” and had to be rebuilt while managing the response.

“New innovations and improved demographic data capture, especially for marginalized and underserved communities, that is integrated into public health data systems will be key to responding to the pandemic and future health inequities,” Mimi and Webb noted in the summary blog post.

Application programming interfaces (APIs), artificial intelligence, and cloud computing technology are set to bring the industry into its next digital iteration, the authors concluded.

“The 21st Century Cures Act and the information sharing provisions in ONC’s Cures Act Final Rule have enabled the first steps to making data available across the healthcare system,” Miri and Webb wrote. “Health IT developers, policy makers, providers, and patients will be able to securely access their information with the broader adoption of standardized APIs.”






Hannah Nelson


Standardized EHR documentation practices for SDOH such as race and ethnicity could help prevent data quality issues that can lead to bias.


Social determinant of health (SDOH) data quality issues signal the need for standardized SDOH EHR documentation practices to avoid bias and promote health equity, according to a study published in JAMIA.

Researchers conducted a review of 76 studies related to SDOH data quality.

The majority of articles that discussed race, ethnicity, or country-of-origin data (65 percent) examined data plausibility, which refers to data accuracy.

Accurate race/ethnicity data is key for clinical research, especially as the industry continues its focus on SDOH data and health equity.

However, researchers noted misclassification bias as a problem or a potential problem in more than half of the articles about race/ethnicity data plausibility. What’s more, several studies reported that implausible data and misclassification errors were more likely for certain groups.

Notably, 14 studies reported that Hispanic patients were more likely to be misclassified in terms of their ethnicity. Patient misclassification includes missing ethnicity information or misclassification into the “Other” category.

Several studies speculated that this may be due to the fluid nature of the definitions of race and ethnicity.

“The fluidity of these definitions leads patients to respond inconsistently to questions about their race/ethnicity, thus causing problems with data reliability,” the literature review noted. “Further, the fact that these categories are so broad and poorly defined leads to difficulties with data validity.”

Misclassification can have profound impacts on clinical research, the study authors noted.

“When patients from one racial or ethnic group are lost in another group or mistakenly categorized as ‘Other,’ subsequent analysis can cause those groups to be under-represented in research results,” they explained. “Misidentification of the race or ethnicity of groups of patients can inadvertently lead to the erasure of those groups from clinical research.”

Several studies speculated that variations in how healthcare organizations collect and record race/ethnicity information have impaired data quality.

“Consistently applied standards for SDOH data collection in the EHR would result in improved data quality, which in turn would lead to more robust research, care coordination, and population health management,” they noted.

The review authors noted that the integration of patient-facing health IT could also help mitigate race/ethnicity misclassification.

“It is possible that the increasing use of dynamic patient-facing data entry tools may allow people to inform and correct their own demographic information, thus helping to improve the quality of race, ethnicity, and country-of-origin data in the future,” the study authors suggested.

The quality of data elements is key in supporting interoperability for large-scale research, data analytics, and care coordination, the review authors emphasized.

“Privacy-preserving record linkage (PPRL) methods identify when records from different sources belong to the same entity while minimizing the exposure of sensitive personal information,” the review authors wrote. “These techniques often rely heavily on patient address along with name and date of birth. When there are errors or missing address data, linkage quality suffers.”

The review authors added that healthcare organizations originally collected demographic data such as race, ethnicity, insurance status, and address, for administrative purposes. Repurposing administrative data for secondary, retrospective research can result in poor data quality.

“Given the increasing importance of social determinants in health equity research and intervention, it is crucial that healthcare institutions work to improve the quality and availability of these data,” the authors noted.

The review revealed several evidence-based solutions to mitigate issues associated with data quality problems. The researchers grouped these recommendations into five main suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.





Hannah Nelson


Using machine learning to extract SDOH data from EHR clinical notes could aid in the development of clinical decision support systems, a study says.


Machine learning offers significant potential to extract social determinant of health (SDOH) data from EHR clinical notes, which may aid in the development of clinical decision support systems, according to a study published in JAMIA.

The researchers conducted a literature review of 82 publications focused on the extraction of SDOH data from EHR clinical notes.

Despite increased interest in capturing SDOH in EHRs, data is typically locked in unstructured clinical notes, the study authors explained.

In general, the researchers observed two major steps associated with SDOH extraction systems from the literature.

“The first step is gathering SDOH-related keywords to create lexicons for each SDOH category, and the second step is developing rule-based or supervised systems to locate clinical notes associated with SDOH categories or extract SDOH concepts,” they wrote.

Rule-based approaches require manual chart review while supervised systems leverage machine learning approaches, the researchers explained.

In total, 22 out of 82 publications used rule-based methods to identify SDOH in clinical notes. Health IT researchers leveraged rule-based systems more frequently for housing, transport, and social isolation.

On the other hand, researchers used machine learning techniques more frequently for smoking, alcohol, and substance use data extraction efforts.

Insufficient volumes of structured data for social support and homelessness may explain why rule-based systems were more common for these variables, the researchers suggested.

The study authors pointed out that integrating automated SDOH data extraction systems into EHRs may aid in clinician burden.

“In clinical settings, providers report spending less time on patient care and more time on administrative burdens that are byproducts of data management in the EHR,” they wrote. “Manual screening of SDOH could potentially further complicate and delay the process for healthcare staff.”

“We believe that the NLP-based SDOH identification and the developed outcome analysis tools may offer an optimal solution that may minimize impact on current documentation routines while guiding providers to make better, informed and holistic clinical decisions, they explained.

The researchers also noted that as SDOH categories grow in number and complexity with the industry’s focus on health equity, storing SDOH in a structured framework could become inefficient and require frequent maintenance.

“With increasing recognition of nonclinical factors that define patients’ health risks, needs, and outcomes, it becomes equally imperative that social and behavioral concepts are captured in order to be leveraged during clinical decision-making related to diagnosis and therapy planning,” they wrote.

“Devising novel ways in which such data can be extracted and leveraged with as little impact on current documentation routines of providers is an ideal solution,” the authors continued. “With the valuable knowledge of the relatively new literature in this area, researchers can leverage such reviews to steer their study in innovative ways.”

The researchers noted several opportunities for future NLP research that extracts less-studied SDOH such as child and sexual abuse, financial issues, transportation, neighborhood, social isolation, family problems, employment, education, food insecurity, and access to healthcare.

“Another interesting study would be to compare different aspects of NLP algorithms, such as system performance, amount of annotated data, type of NLP systems, and so forth with the difficulty of SDoH extraction,” the researchers added.





Hannah Nelson


A primary care EHR integration helped boost referrals, especially in settings with high clinician engagement.


EHR integration in primary care settings can increase referrals, but use of that health IT usually varies based on clinician engagement, according to a recent study published in the Annals of Family Medicine.

The study, which focused specifically on EHR integrations that help refer patients with hearing loss to audiologists, underscores the importance of clinician engagement in health IT use.

Primary care physicians (PCPs) rarely screen for hearing loss due to time constraints, conflicting priorities, and limited experience managing hearing loss, the study authors explained.

The researchers implemented an EHR integration at 10 clinics across two health systems to see if EHR prompts would boost PCP assessment of hearing loss.

The EHR implementation set off an EHR prompt to screen for hearing loss among patients ages 55 years or older, asking a validated one-question screener.

Overall, audiology referrals increased from 2.2 percent to 11.5 percent.

“This prompt may have reduced some of the uncertainty clinicians feel around hearing loss, thereby increasing their screening and referral rates,” the study authors suggested.

Through clinic observations and semistructured interviews with 27 family medicine clinicians, the study authors found several main themes.

First, the researchers noted that clinicians found the prompt overwhelmingly simple to use and considered it an effective way to increase conversations with patients about hearing loss.

“Our results indicated that the EHR prompt was easy to use and incorporate into typical workflows, likely because it was designed through extensive feedback from PCPs,” the study authors explained.

Next, the study revealed that clinician engagement and buy-in played a vital role in implementation of the health IT tool. For example, clinicians who shared personal experiences with hearing loss described strong support for the EHR integration, the study authors explained.

“They often emphasized the difficulty of addressing their own family members’ hearing loss due to an unwillingness to admit hearing loss or wear hearing aids,” the researchers wrote. “They viewed the prompt as an opportunity to discuss the impact of hearing loss on quality of life with patients.”

The research also revealed that medical assistant (MA) involvement in the EHR hearing loss prompt workflow varied by health system, clinic, and clinician.

Some physicians reported unclear expectations about how staff and clinicians should interact with the prompt. For instance, at one health system, MAs were responsible for addressing prompts related to colorectal and breast cancer screenings. The authors suggested that systematically leveraging MAs could be an effective way to increase the utilization of screening prompts in primary care.

“Moreover, expanding MA responsibilities may lead to innovations for panel management, health coaching, or patient navigation that reduce patient-level barriers to hearing loss,” the researchers added.

The researchers also found that clinicians prioritized the prompt during annual wellness exams compared to acute visits when there were more pressing health needs and concerns.

Lastly, while the prompt resulted in more conversations about hearing loss and more referrals, clinicians reported uncertain impact on patient outcomes.

“Clinicians were unsure whether the prompt had increased use of hearing aids, though most expected to see an increase over time,” the study authors wrote. “Many reported that additional barriers, particularly cost, would limit the number of patients who obtained hearing aids.”

“Longitudinal research is needed to know whether a prompt leads to improved hearing loss screening and referral over time,” the researchers noted.







Hannah Nelson


Machine learning systems can mitigate burden and boost EHR usability for disease phenotyping to support clinical research, according to a new study.


Machine learning systems can aid EHR usability and cut burden for disease phenotyping to support clinical research, according to a recent Mount Sinai study published in the journal Patterns.

The machine learning-based algorithm diagnosed patients as accurately as the standard set of disease phenotyping algorithms for conditions like dementia, sickle cell anemia, and multiple sclerosis.

“There continues to be an explosion in the amount and types of data electronically stored in a patient’s medical record,” Benjamin S. Glicksberg, PhD, a senior author of the study, said in a press release. “Disentangling this complex web of data can be highly burdensome, thus slowing advancements in clinical research.”

“In this study, we created a new method for mining data from electronic health records with machine learning that is faster and less labor intensive than the industry standard,” continued Glicksberg, an assistant professor of genetics and genomic sciences and a member of the Hasso Plattner Institute for Digital Health at Mount Sinai (HPIMS).

Clinical research scientists currently use a standard set of disease phenotyping algorithms managed by a system called the Phenotype Knowledgebase (PheKB).

The study authors noted that while effective, implementing a PheKB algorithm on a new dataset is time-consuming as it requires variably formatted data, as well as specific laboratory or clinical information.

PheKB algorithms also have limited scalability since they are curated based on expert knowledge for one disease at a time, the researchers explained.

Only 46 diseases or syndromes are represented by public PheKB algorithms as of July 2020.

To develop a new algorithm for a disease, researchers must manually go through EHR data looking for certain data that is associated with the disease and then program an algorithm to identify patients with those disease-specific pieces of data.

The Mount Sinai researchers automated the disease phenotyping process through machine learning in an effort to save clinical researchers time and effort.

The researcher teams’ new method, Phe2vec, was based on studies they had already conducted.

“Previously, we showed that unsupervised machine learning could be a highly efficient and effective strategy for mining electronic health records,” explained Riccardo Miotto, PhD, a former assistant professor at the HPIMS and a senior author of the study.

“The potential advantage of our approach is that it learns representations of diseases from the data itself,” Miotto continued. “Therefore, the machine does much of the work experts would normally do to define the combination of data elements from health records that best describes a particular disease.”

Glicksberg noted that the study’s promising results suggest the algorithm could be used for large-scale phenotyping of diseases in EHR data.

“With further testing and refinement, we hope that it could be used to automate many of the initial steps of clinical informatics research, thus allowing scientists to focus their efforts on downstream analyses like predictive modeling,” he said. “We hope that this will be a valuable tool that will facilitate further, and less biased, research in clinical informatics.”

The study authors said that they plan to analyze how phenotypes change over time. They also plan to embed other kinds of data, such as genetics and clinical imaging, into the framework for refined disease phenotyping.

Additionally, they intend to explore the use of the system to create reliable disease-specific control cohorts for observational studies.






Hannah Nelson


Four of six travel intensive care unit nurses hired by a California hospital to address the ongoing COVID-19 surge quit due to poor EHR use training.


Four travel nurses hired by a California hospital to help address the ongoing surge of COVID-19 strains quit days after they were hired due to inadequate EHR use training and onboarding issues, according to reporting from the Times Standard.

Providence St. Joseph Hospital in Eureka, California brought on eight new traveling caregivers last week—six intensive care unit RNs and two respiratory therapists—according to a Wednesday press release. Four out of the six nurses quit the very next day, according to the Times Standard.

Ian Seldon, a spokesperson with the California Nurses Association, told the news outlet that the nurses left St. Joseph Hospital due to inadequate EHR training.

“Apparently, the travelers were met without necessary resources, including access to the unit’s electronic charting system and were immediately handed full patient assignments with little in the way of orientation,” Seldon noted. “So, four out of the six (travel nurses) quit.”

“In the words of one of them, the travelers were ‘thrown to the wolves’ and with all the opportunities available to travelers these days, they just didn’t come back,” Seldon explained.

Roberta Luskin-Hawk, MD, chief executive for Providence in Humboldt County, told the news outlet that the nurses’ departure was “an unfortunate and unique circumstance.”

“Some of the travelers who came to us through our request to the Medical Health Operational Area Coordinator did not stay at our hospitals,” she said. “The primary reason was that they were not familiar with our electronic medical record system — a system that is used by many hospitals.”

“Additionally, there were issues with the onboarding of these caregivers which created a challenge for them acclimating to our hospital,” she continued.

Luskin-Hawk said that Providence would continue to work with the Medical Health Operational Area Coordinator to find additional staff for St. Joseph Hospital as well as Redwood Memorial Hospital in Fortuna.

“We will continue, as we have throughout the pandemic, to aggressively seek additional resources focused on supporting our caregivers as they respond to the large number of patients requiring hospital services as part of this COVID surge while caring for our community’s important health care needs from open-heart surgery and trauma care to cancer care,” she said.

Luskin-Hawk also noted that the healthcare organization would be transitioning to a more popular EHR system to enhance care delivery across the health system.

“In addition to meeting the immediate needs of our communities, we are excited to be transitioning to a more widely used electronic medical record system in the coming weeks and will continue to work on additional projects that will enhance our health care delivery system over the near term and for years to come,” Luskin-Hawk told the Times Standard.

Effective EHR training programs may be the key to clinician satisfaction, according to a recent KLAS survey.

Researchers recommended healthcare industry stakeholders implement standards to ensure clinicians across health systems receive high-quality EHR training. They recommended at least four hours of EHR training to improve EHR satisfaction throughout the industry.

“Organizations requiring less than 4 hours of education for new providers appear to be creating a frustrating experience for their clinicians,” wrote the KLAS researchers. “These organizations have lower training satisfaction, lower self-reported proficiency, and are less likely to report that their EHR enables them to deliver quality care.”

Investing in EHR training may make the user more proficient at navigating the EHR, learning the intricacies of the platform, and it could potentially reduce the chances of clinician burnout in the future.  

“For EHR software to revolutionize health care, both the software and the use of that complicated software need to progress in parallel,” the research team concluded.





Hannah Nelson


API EHR integrations can support patient centered care by promoting patient reported outcomes data sharing with primary care providers.


Application programming interface (API) EHR integrations can support patient reported outcomes data sharing with primary care providers, according to a study published in JAMIA.

In a prior feasibility study, researchers developed and tested an initial prototype of a remote patient monitoring application for asthma patients in pulmonary subspecialty clinics. The original intervention consisted of a smartphone app that prompted patients to report asthma symptoms every week. The study demonstrated high patient adherence and low provider burden.

Increased access to patient reported outcomes data can aid providers in delivering patient-centered care.

For the current study, researchers adapted the intervention to the primary care setting and gathered patient and PCP feedback on requirements for a successful remote patient monitoring application.

The study’s results are based on analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs.

PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. Nurse-facing requirements included callback requests sent as an EHR inbox message.

Patient-facing requirements included the ability to complete a one- or five-item symptom questionnaire each week, depending on asthma control. Patients also called for the option to request a callback, and the ability to enter notes. Additionally, patients suggested that the app push tips prior to a PCP visit. Requirements were consistent for English- and Spanish-speaking patients.

EHR integration of the intervention required the use of custom APIs, the authors noted.

“This study demonstrates how third-party apps can be used for PRO-based between-visit monitoring in a real-world clinical setting with the goal of maximizing use, usability, and scalability in parallel with native EHR functionality and patient portal offerings,” the study authors wrote.

“Although we focused exclusively on asthma, these findings may generalize to other chronic conditions that benefit from routine symptom monitoring using standardized PROs, such as rheumatologic disease, mental health illness, and irritable bowel disease,” they continued.

Additionally, the authors noted that their study’s findings could be applied more broadly to support primary care patient reported outcomes for patients with multiple chronic illnesses.

“Similar requirements elicitation approaches also have the potential to develop scalable interventions for monitoring overall health of patients with multiple chronic conditions, such as captured by global health PROs which measure general physical, mental, and social health,” they wrote.

“With further testing, iterative development, and continued attention to scalability, the rapidly evolving efforts of digital remote monitoring between visits may be achievable at the population level for patients with chronic conditions,” the authors continued.

As care is increasingly delivered remotely, such requirements are likely to become more important.

“Our effort is distinct from other reported efforts at developing clinically integrated remote monitoring interventions, which lack prioritization of requirements, require additional clinical staff, such as care managers, to monitor data, or require a device,” the authors wrote.

These alternate approaches may encounter scalability issues, such as cost challenges for using devices where they aren’t necessary, they explained.

“Furthermore, we provide new knowledge regarding how a third-party application can be integrated into an EHR with patient- and provider-facing components to enable the use of PROs for between-visit monitoring.”





Erin McNemar


How de-identified data can advance medical research and improve patient care.


De-identified data has become an important tool in medical research and for providers looking to enhance patient care. While data sharing between different organizations could violate the Health Insurance Portability and Accountability Act of 1996 (HIPAA), the de-identification process makes sharing information HIPAA-compliant.

De-identified data sharing can then assist medical researchers in advancing tools and treatments. Additionally, it allows for collaborative efforts from large provides. Overall, de-identifies plays a critical role in improving the patient experience.

WHAT IS DE-IDENTIFIED DATA IN HEALTHCARE?

The process of de-identification removes all direct identifiers from patient data and allows organizations to share it without the potential of violating HIPAA.

Direct identifiers can include a patient’s name, address, medical record information, etc. While direct identifiers are removed from the data to keep a patient’s identity confidential, indirect identifiers can remain untouched to allow researchers to study data trends. Indirect identifiers include gender, race, age, etc.

According to the  Department of Health & Human Services,  “The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.”

Data de-identification is crucial to advancing medical research and treatment while also protecting patient privacy.

HOW DO RESEARCHERS USE DE-IDENTIFIED DATA?

De-identified data can be used in medical research and treatment. Once identifying information is removed, the data can provide useful information for advancing healthcare.

In a recent study, researchers used de-identified data to develop an artificial intelligence tool to predict 30-day mortality risks in patients with cancer. Cancer is one of the leading causes of death in the United States each year. With the artificial intelligence tool, medical professionals can discover patients who are at high risk and provide early intervention and resolutions for reversible complications.

Additionally, the tool can identify patients who are approaching end of life (EoL) and refer them to early palliative and hospice care. In this case, the use of de-identified data assists with artificial intelligence and can provide an improved quality of life and symptom management for the patient.

“In contrast, aggressive, life-sustaining EoL care can conflict with patient preference and result in lower quality of life, family perceptions of poorer quality of care, and greater regret about treatment decisions. Earlier referral also represents an opportunity to transform cancer care by reducing the potential for unnecessary, toxic and expensive treatments at EoL,” the study authors wrote.

De-identified data can also be used in developing predictive analytics tools. To address healthcare gaps created by the COVID-19 pandemic, UnitedHealthcare developed a predictive analytics tool that used de-identified data to address social determinants to health.

“Around 80 percent of your health is determined by things that are not your genetics. There are things more such as what’s going on in the rest of your life, what we call social determinants of health — social, economic, gender orientation, and other markers that sometimes can lead to inequality,” Rebecca Madsen, chief consumer officer, UnitedHealthcare said.

To eliminate care gaps, UnitedHealthcare created an advocacy system to assist members who might be struggling due to their social environment. Through predictive analytics and a machine learning model, the advocacy system can evaluate de-identified data from members and determine the need for social services.

Data is then loaded into an agent dashboard used by UnitedHealthcare advocates. When a member calls in, advocates can connect the caller to community resources at low or no cost.

De-identified data allows medical professionals to both develop tools to better serve patients and advance research to produce improved outcomes.

WHAT ARE THE BENEFITS OF DE-IDENTIFIED DATA?

Data sharing allows those in the healthcare field to create better tools and treatments to improve patient care and outcomes. However, according to the Centers for Disease Control & Prevention (CDC),  HIPAA law states that patient information must be protected and cannot be shared with other entities without the patient’s knowledge and consent.

By de-identifying data, providers can share information with other organizations to advance medical researcher and treatment. Additionally, de-identifying the data removes some liability regarding HIPAA violations.

Furthermore, the use of de-identified data allows for the collaboration of large data analytic platforms. Earlier this year, fourteen leading healthcare providers partnered to form Truveta, a new company that used big data analytics to enhance care insights.

The providers included AdventHealth, Advocate Aurora Health, Baptist Health of Northeast Florida, Bon Secours Mercy Health, CommonSpirit Health, Hawaii Pacific HealthHenry Ford Health SystemMemorial Hermann Health SystemNorthwell Health, Novant Health, Providence health system, Sentara Healthcare, Tenet Health, and Trinity Health.

By combining the healthcare providers’ tens of millions of patients and from thousands of care facilities across 40 states, Truveta created a large de-identified dataset for their analytic effort.

With de-identified data, providers can share patient data to assist in medical advances while also maintaining patient privacy and complying with HIPAA.