Christopher Jason

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.”

Christopher Jason

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.

Christopher Jason

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.

Christopher Jason

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.  

Christopher Jason

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.”

Christopher Jason

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.  

Christopher Jason

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.”

Christopher Jason

API adoption can streamline patient access to data, promote the use of clinical decision support tools, and boost both interoperability and patient data exchange between providers.

Application programming interface (API) adoption in healthcare will give clinicians and patients access to patient data and allow third-party applications access patient information and boost patient care, according to Ben Moscovitch, project director of Health Information Technology at Pew Charitable Trusts.

Although EHR adoption is becoming more widespread throughout the healthcare industry, interoperability and patient data sharing still pose challenges to providers.

As a result, a portion of the ONC final rule calls on medical providers and device developers to promote patient data access using third-party apps and APIs.

ONC proposed to adopt the HL7 Fast Healthcare Interoperability Resources (FHIR) standard as a foundational standard and requested comment on four options to determine the best version of FHIR to adopt.

Ultimately, ONC adopted FHIR Release 4.

Moscovitch said to address interoperability and patient data exchange challenges, the healthcare industry can adopt similar a technological approach that industries, such as finance and travel, have adopted. For example, APIs allow travel services to compare flights from separate airlines without the user visiting each website.

“If standard APIs were broadly adopted in health care, patients could access and compile their data from multiple providers while clinicians could process complicated information and make care recommendations,” Moscovitch said. “APIs would also offer other benefits, such as facilitating the exchange of clinical data among health care providers.”


Moscovitch gave three key healthcare benefits for API adoption, which include patient access to data, the incorporation of clinical decision support (CDS) tools for prescribing antibiotics, and patient data exchange between providers.

API adoption can give patients access to data. Patients can utilize APIs to track and manage their healthcare outside of the doctor’s office on their smartphone or computer.

Using APIs, providers can integrate applications into the EHR to give users a broader range of CDS tools that would allow the user to pick one that works best.

APIs can allow clinicians to pick and choose the needed or important patient information to exchange, rather than sending full clinical history


Although APIs can be beneficial, health IT professionals still face integration and usability challenges.

First, health IT developers could increase API adoption if they code them differently for each EHR system. But this approach would be limited because it would only allow adoption on one EHR system or application, Moscovitch said.

“But standards—such as the industry-developed Fast Healthcare Interoperability Resources (FHIR), which is a standard for exchanging health care information electronically—ensure easier use of APIs,” explained Moscovitch. “FHIR can offer access to individual pieces of information—such as a list of medications—instead of a broader document containing more data, some of which might be unnecessary or patients may not wish to share.”

Additionally, not all APIs can read EHR data and most do not have write access functions to input information back into the EHR. Moscovitch said once APIs receive write access functions, clinicians and patients could utilize CDS tools to edit portions of the EHR, such as changing an address, correcting errors, or updating symptoms.

“Federal regulations finalized in 2020 require the use of FHIR and expand the dataset that must be available for exchange via APIs, but the rules did not address write access,” Moscovitch explained.

“Those regulations are scheduled to take effect in 2022. Although these regulations are critical, they do not mitigate the need for significant additional policy and technology developments in order to successfully integrate and prioritize APIs within health care. Policymakers can take additional measures to promote the use of APIs and incentivize new capabilities through both legislation and additional regulation,” Moscovitch continued.


Pew partnered with RTI International, a research institute, to analyze and evaluate the current and future API uses by interviewing health IT professionals.

Respondents said they most commonly utilized APIs for patient access and CDS. Some respondents even said they have not utilized APIs for other use cases, such as patient data exchange.

Next, respondents said health IT vendors vary on the permitted data elements for patient data exchange. This variance impacts the type and amount of patient information that clinicians can exchange.  

Pew learned many of the terms and conditions from providers, EHR vendors, and third-party app developers were incomplete and did not have critical details, such as costs. Because of this, those three groups of individuals can dictate costs and these costs vary.

Last, the respondents said API use could improve by enabling EHR data entry, integrating applications into clinician workflow, and implementing standardized data elements.

In order to accelerate API adoption and enhance both security and usability, Moscovitch recommended lawmakers focus on developing policies that:

  • Advance privacy and security
  • Develop the ability to enter data into EHRs
  • Grow API use for data exchange among providers
  • Expand more data elements for exchange
  • Monitor costs
  • Protect health inequities with CDS tools

“Increased use of APIs—particularly those based on common adopted and consistently deployed standards—has the potential to make health care more efficient, lead to better care coordination, and give providers and patients additional tools to access information and ensure high-quality, efficient, safe, and value-based care,” Moscovitch concluded.

“Yet obstacles remain, such as some hospital hesitation to grant patient access to data, lack of bidirectional data exchange, confusion around the process of implementing APIs, and potentially prohibitive fee structures.”

Jessica Kent

A predictive analytics algorithm can determine which COVID-19 patients are most likely to deteriorate while in the hospital.

Using predictive analytics, researchers were able to accurately identify COVID-19 patients at risk of quickly deteriorating up to 24 hours in advance, a study published in British Journal of Anaesthesia revealed.

Throughout the pandemic, the only constant has been the virus’s wildly different impact on individual patients. While some present with only mild respiratory symptoms, others have severe illness and need supplementary oxygen or ventilators.

Researchers noted that using invasive mechanical ventilation to treat COVID-19 respiratory failure have shown mortality greater than 85 percent. However, there is little information available about which patients admitted to the hospital who don’t require mechanical ventilation will progress to mechanical ventilation. Researchers also have limited data on which clinical factors are associated with that progression.

“You can see large variability in how different patients with COVID-19 do, even among close relatives with similar environments and genetic risk,” said Nicholas J. Douville, of the Department of Anesthesiology, one of the study’s lead authors. “At the peak of the surge, it was very difficult for clinicians to know how to plan and allocate resources.”

To build the predictive analytics algorithm, researchers from Michigan Medicine looked at a set of patients with COVID-19 hospitalized during the first pandemic surge from March to May 2020 and modeled their clinical course.

The team used inputs such as a patient’s age, whether they had underlying medical conditions, what medications they were on when entering the hospital, and variables that changed while hospitalized – including vital signs like blood pressure, heart rate, and oxygenation ratio. The group aimed to discover which of these data points would help best predict which patients would require a mechanical ventilator or die within 24 hours.

Of the 398 patients in the study, 93 required a ventilator or died within two weeks. The model was able to predict mechanical ventilation most accurately based on key vital signs, including oxygen saturation ratio, respiratory rate, heart rate, blood pressure, and blood glucose level.

The team examined data points of interest at four-, eight-, 24-, and 48-hour increments to identify the optimal amount of time necessary to predict and intervene before a patient deteriorates.

While the algorithm worked best at shorter increments, researchers noted that the model maintained accuracy even two days before an adverse event.

“The closer we were to the event, the higher our ability to predict, which we expected. But we were still able to predict the outcomes with good discrimination at 48 hours, giving providers time to make alterations to the patient’s care or to mobilize resources,” said Douville.

For example, the algorithm could quickly identify a patient on a general medical floor who would be a good candidate for transfer to the ICU, before their condition deteriorated to the point where ventilation would be more difficult.

Going forward, the team expect that the algorithm can be integrated into existing clinical decision support tool already used in the ICU. In the short term, the study highlights patient characteristics that clinicians should consider when treating patients with COVID-19.

The study also raises new questions about which COVID-19 therapies, such as anti-coagulants or anti-viral drugs, may or may not alter a patient’s clinical trajectory.

“Our algorithm can be integrated into a clinical support software with the ultimate goal of identifying patients prior to clinical decompensation. Our primary target (24-hour prediction window) was selected to allow appropriate time for interventions, while still providing evidence of deterioration in dynamic features,” researchers said.

The algorithm could help providers better manage patient care and allocate necessary resources.

“While many of our model features are well known to experienced clinicians, the utility of our model is that it performs a more complex calculation than the clinician could perform ‘on the back of the envelope’ – it also distills the overall risk to an easily interpretable value, which can be used to ‘flag’ patients in a way so they are not missed,” said Douville.

Jessica Kent

At the Alliance for Better Health, an interoperable digital platform is connecting providers and community leaders to effectively address individuals’ social determinants of health.

In care delivery today, it’s well understood that a patient’s social determinants of health have a profound impact on both physical well-being and healthcare spending – sometimes even more so than clinical factors.

Evidence has shown that industrialized nations that dedicate more resources to social services than healthcare tend to have better health outcomes.

A 2019 report from the National Academies of Sciences, Engineering, and Medicine revealed that for every $1 the US spends on healthcare services, it spends about 90 cents on social services. In comparison, other industrialized countries spend $2 on social services for every $1 they spend on healthcare. 

Although the importance of addressing individuals’ social needs is widely known, many providers still struggle to identify and document the non-medical factors affecting patients’ health. And even if they can, clinicians then have to clear the next difficult hurdle: referring patients to the right services that will meet their needs.

“When I was a family doctor and a patient would tell me about a need that our organization didn't address – such as housing instability, food insecurity, or transportation challenges – often I would look something up quickly on the internet and then scribble the number of some kind of service on a yellow sticky note,” Jacob Reider, MD, CEO of the Alliance for Better Health, told HealthITAnalytics.

While better than nothing, this method was not exactly effective, Reider said. There was no way for providers to tell whether a person actually received the services that were recommended to them. Clinicians also had no way of knowing if the referred organization could adequately meet a patient’s needs.

To help clinicians communicate more easily with social service entities, Reider and his team partnered with digital referral platform Unite Us, implementing their platform to develop Healthy Together, a closed-loop network that connects physicians, organizations, and community members in one platform. The tool allows Alliance to quickly identify and address social determinants of health, eliminating silos between each party.

“Healthy Together solves a problem that many communities have – and are our community was certainly one of those many,” said Reider.

“At Alliance, we recognized the gap between services needed and services provided. A referral is open forever because we were never tracking whether or how the loop was closed. Our platform makes sure that the loop does get closed.”

The first step in developing the platform was to find social organizations that would pledge to respond to requests for services, Reider noted.

“Healthy Together is a commitment from all participating organizations to send referrals and to receive referrals. If a referral is received and the services provided, the organization needs to document that in the system so that we know the referral was closed,” he stated.  

“And if the referral wasn't closed for any reason, we want them to say the service wasn't provided. Either that individual didn't appear, or they were hard to reach, or perhaps that's not the right organization. We did this so we could make sure that people receive the services they need, when they need them.”

The platform also ensures that the people who administer these services can easily access and update relevant information.

“There are clinicians who access it, but more often it’s care coordinators or social workers who work in either hospitals or medical practices,” Reider said.

“Community organizations access the platform as well, including food pantries, homeless shelters, job assistance providers, and substance use disorder treatment facilities. They all have logins and they can both send and receive referrals through the system.”

With all of these different players accessing and updating the tool, interoperability is a critical part in the successful design and deployment of these platforms, Reider said.

“In communities where we need to communicate both the need and the services, you need a common infrastructure and interoperable systems to make all of that work,” he said.

Industry-wide standards are also necessary to help organizations address patients’ social determinants – a refrain that has been echoed by leaders in all sectors of healthcare.

“Step two is implementing standards so that the systems can talk to each other. Without standards, it’s really hard to make that happen,” Reider explained.

“If you're looking into a solution like this, you need to ask the question, are we using industry standards? How interoperable is the system? And that's very different from how integrated the system is. Integrations don't use industry standards, whereas interoperable components do and are much easier to maintain.”

In addition to establishing a common infrastructure and standards, leaders will need to determine how they want to measure the value of this kind of platform.

“The first step was defining success, and one definition of success is that the loop is closed and the service was provided. When we started, we were seeing service provision rates under 50 percent. That means that if I refer a hundred people for services, less than 50 of them got those services,” Reider said.

“We are now measuring that rate on a daily, weekly, and monthly basis. We're now in the upper seventies of percentages, which is significant when compared to other communities.”

Alliance decided to use another measure of success as well: the health of the community.

“We’ve started measuring how frequently individuals have to go to the emergency department for things that generally would not warrant an ER visit. An easy example is asthma exacerbation in a child. Kids shouldn't ever have to go to the hospital for asthma. If they have to go to the hospital for asthma, that means that their asthma is not controlled,” said Reider.  

“If their asthma isn't controlled, then there's a problem. And likely the problem stems from their environment. Maybe they couldn’t make it to the pharmacy and get their medications because there was a snow storm and they had transportation challenges. There are social issues that have medical consequences. When we measure the medical consequences, we can get a broader perspective on whether we are succeeding.”

Through the use of platforms like Healthy Together, healthcare leaders can partner with community organizations to better understand and meet patients’ social needs.

“We developed this platform because we recognized that none of us meets all the needs of an individual. Instead, it takes a whole community to meet all the needs of a patient, and we need to share that responsibility. Healthy Together prompts folks to think more broadly about how we serve people, and then makes it easy for them to act on those expectations,” Reider concluded.

Christopher Jason

CTHealthLink added Yale New Haven Health and UConn Health 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 two significant health systems to its network, Yale New Haven Health and UConn Health.

“The connections to Yale New Haven and UCONN are important milestones for Connecticut physicians and their patients,” Robert Aseltine, MD, UConn Health professor and chair of CTHL Advisory Board, said in a statement. “These connections allow Connecticut healthcare providers to gain access to critical patient data from hospitals, clinics, and practices, data that are needed to provide safe and comprehensive care to their patients.”

Yale New Haven Health and UConn Health join 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. The two organizations also join the state’s public health registries.

Additionally, the HIE has connected to the Carequality interoperability network and is a KONZA National Network member, enabling patient data exchange from across the country.

“Data sharing across providers and facilities is particularly important when patients are transferred from their home communities to receive care, which is becoming more common as COVID-19 strains hospital capacity,” Aseltine continued. “Having immediate access to a patient’s full medical record under these conditions may save lives and significantly improve health care for Connecticut patients.”

Adding two more connections increases patient data exchange and interoperability across the state, triggering a more effective response to certain health emergencies, including the COVID-19 pandemic.

“Connecticut cannot wait any longer for the meaningful exchange of patient data,” said Jeffrey Gordon, MD, chair of the CSMS Council. “In the face of the COVID-19 pandemic, Connecticut physicians are facing unprecedented hurdles to providing quality medical care.”

“Physicians throughout Connecticut must have the ability to coordinate not only COVID -19-related medical care, but also COVID-19 vaccinations. The time for health data exchange to be operational in Connecticut is not tomorrow, but today,” Gordon continued.

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.

In May, the state of Connecticut signed CTHealthLink as the first member of the state’s health information exchange.

Over the past decade, state leaders found it was not easy to launch a statewide HIE. In fact, the state attempted to launch the HIE four times before adding CTHealthLink, costing the state millions of dollars.

Now established, experts say the HIE will reduce costs and improve care by eliminating the chances of duplicative testing, link several providers without going through the process of establishing a connection with each facility, and identify health trends.

It also presents financial benefits for the state. Health systems utilizing Medicaid and Medicare services can only receive payments if they can show that they are improving the quality of care and reducing hospital readmissions. Better care coordination, enabled by a functional HIE, could help organizations accomplish those clinical quality metrics.

Looking forward, the two organizations plan to improve patient care, boost interoperability throughout the state, and enhance Connecticut’s healthcare delivery system.

Since the HIE is still in its early stages, Aseltine said it will expand upon partnerships with other national exchanges in a way that provides a powerful demonstration of the scale they can achieve together.

“This echoes how important health data exchange is for physicians across the state of Connecticut,” added Layne Gakos, JD, General Counsel of Connecticut State Medical Society.

“We're excited to be where we are right now and to be the first one that's up and running. It's taken a lot of work. But it's been rewarding, and we believe it's going to be rewarding moving forward as the state moves forward in developing its HIE.”

Christopher Jason

Although health IT developers and public health systems were caught off guard by COVID-19, both should be ready for the next wave or a future pandemic.

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 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.

“The current state of COVID-19 data reflects a patchwork of uncoordinated, temporary fixes to a historically neglected public safety function,” wrote the researchers. “As the US enters its second decade of nationally-coordinated digital infrastructure for healthcare delivery and to modernize patient care, COVID-19 has demonstrated that this infrastructure is inadequate to respond to public health emergencies.”

Researchers analyzed the COVID-19 response efforts from 15 health organizations that saw delays in correctly understanding, predicting, and mitigating the COVID-19 spread. The research group focused on EHR data. They also outlined the current health IT infrastructure, such as data registries and clinical data networks, and data ecosystem challenges that are relevant to the current pandemic.  

Through that analysis, the research team determined a number of steps that could help organizations in the current and future steps to mitigate the pandemic, which most experts say is in its third wave. 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 began.

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.

“While public health tools for horizon scanning, disease surveillance, epidemiological modeling, capacity planning, ‘hot spotting,’ and targeted intervention strategies (such as isolation or contact tracing in the case of a transmissible pathogen) use as much available data as possible, the speed with which these data are collected, organized and analyzed is slow,” researchers explained.

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.  

“Unless COVID-19 data initiatives are coordinated and systems are interoperable, much effort and money will be spent into each initiative individually: these initiatives will compete with each other, will only provide partial answers, and will still not properly support public health decision making for this and the next pandemic, and for other diseases that have a large national impact,” explained study authors.  

If developers create new health IT to fill current COVID-19 data needs, it may not be able to be used for a future pandemic, said researchers. Although health IT developers and public health systems were caught off guard by the current pandemic, both should be ready for the next COVID-19 wave or a future pandemic.

Researchers said the value of boosting technology, governance, and an overall strategy can be analyzed through cost and benefits, but many stakeholders must adapt quickly to these three changes because the cost of optimizing a current health IT system can be overwhelming to health organizations.

“We call all stakeholders to act now to build a coordinated system of data sharing to combat COVID-19, and to prepare for the inevitable next pandemic,” wrote researchers.

“Successful implementation of the measures outlined in this article will enable evidence-based approaches to coordinate testing and contact tracing, predict needed resources and prepare accordingly (so “non-essential” healthcare services will not need to be shut down unnecessarily), conduct basic, preventive or therapeutic research, and provide a trusted, factual basis for answering public health questions of critical importance for this pandemic and other health conditions,” concluded the research team.

Christopher Jason

Integrating social determinants of health (SDOH) data into the EHR can help providers and researchers gain insight on COVID-19.

The Gravity Project, a community-led HL7 Fast Healthcare Interoperability Resources (FHIR) Accelerator, published an implementation and recommendation guide for social determinants of health (SDOH) data and terminology, with a focus on food insecurity, housing instability and quality, and transportation access.

Research shows that identifying and implementing a patient’s 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.

Health systems across the country are attempting to implement SDOH data into patient health records. Yet, most health systems face issues, such as interoperability, when trying to implement SDOH into their respective EHRs, meaning there isn’t an abundance of information about what healthcare can do with SDOH data.

With this publication, The Gravity Project developed data elements and standards to gather, exchange, and utilize SDOH data across screening, diagnosis, planning, and intervention platforms.

Founded by the University of California San Francisco (UCSF) Social Interventions Research and Evaluation Network (SIREN) in 2018, Gravity Project consists of over 1,000 healthcare stakeholders. These stakeholders include academic and federal food insecurity experts, community-based organizations, payers, patients, providers, and health IT vendors.

The spread of COVID-19 highlighted the importance of SDOH data collection and integration, making it an area of extreme focus for providers and laboratories.

COVID-19 data from OCHIN, an Oregon-based nonprofit health information network, reported Black patients were 2.5 times more likely than White patients to have a COVID-19 diagnosis observed in the EHR. Additionally, Hispanic patients were two times as likely as Caucasian patients to have a COVID-19 diagnosis listed.

The researchers also noted homeless, or those in housing were almost two times more likely to test COVID-19 positive.

“The Gravity Project’s work to document and integrate social risk in clinical care has never been more urgent than now,” said Tom Giannulli, chief medical information officer of the American Medical Association (AMA).

“With COVID-19, doctors see the intersection of social determinants and health status daily. The AMA is proud to contribute our expertise and to sponsor Gravity’s critical work.”

Gravity Project aims to expand the way healthcare cares for all individual and community needs by capturing and exchanging SDOH data.

Regenstrief Institute, the ICD-10 Coordination and Maintenance Committee, and SNOMED International will help Gravity Project translate consensus data recommendations on food insecurity into code for integration.

Gravity Project noted Regenstrief’s COVID-19 standardized codes for laboratory testing and clinical observations to the Logical Observation Identifiers Names and Codes (LOINC) dataset as the gold standard of data integration.

Looking forward as a separate HL7 FHIR Accelerator project, Gravity Project is gathering the healthcare community’s consensus on data elements and developing a FHIR Implementation Guide for health IT professionals to use as a guide for 2021 implementations.

By the time the healthcare sector launches The Office of the National Coordinator for Health Information Technology’s (ONC) and Centers for Medicare & Medicaid Services’ (CMS) interoperability rules in January 2021, Gravity Project will have data ready for integration on food, housing, and transportation.

“Highmark remains focused on the health and vitality of the communities we serve,” Deborah Donovan, executive committee member of The Gravity Project and vice president of Social Determinants of Health Strategy and Operations at Highmark.

“The Gravity Project’s development of data standards and exchange of SDOH data will be critical to our ability to understand the social needs of our members, patients and communities, and make decisions that best support our customers.”

Mike Miliard

From genetic sequencing to symptom tracking to vaccine development, machine learning algorithms have been instrumental in helping uncover hidden clues about the novel coronavirus, says Cris Ross.

In his opening keynote Tuesday at the HIMSS Machine Learning & AI for Healthcare Digital Summit, Mayo Clinic CIO Cris Ross enumerated some of the many ways artificial intelligence has been crucial to our evolving understanding of COVID-19.

Way back in March, for instance, researchers were already using an AI algorithm – trained on data from the 2003 SARS outbreak – for "a recurrent neural network to predict numbers of new infections over time," he said. "Even from the beginning of COVID-19, artificial intelligence is one of the tools that scientists have been using to try and respond to this urgent situation."

And just this past month, Boston-based nference – whose clinical-analytics platform is used by Mayo Clinic – sifted through genetic data from 10,967 samples of novel coronavirus. Along the way, researchers discovered "a snippet of DNA code – a particular one that was distinct from predecessor coronaviruses," said Ross. "The effect of that sequence was it mimics a protein that helps the human body regulate salt and fluid balances.

"That wasn't something that they went looking for," he said. "They simply discovered it in a large dataset. It's since been replicated and used to support other research to discover how genetic mutations and other factors are present in COVID-19 that help, both with the diagnosis of the disease, but also its treatment."

Many other now commonly understood characteristics of the novel coronavirus – the loss of smell it can cause, its effects on blood coagulation – were discovered using AI.

Around the world, algorithms are being put to work to "find powerful things that help us diagnose, manage and treat this disease, to watch its spread, to understand where it's coming next, to understand the characteristics around the disease and to develop new therapies," said Ross. "It's certainly being used in things like vaccine development."

At the same time, there are already some signs that "we need to be careful around how AI is used," he said.

For example, the risk of algorithmic bias is very real.

"We know that Black and Hispanic patients are infected and die at higher rates than other populations. So we need to be vigilant for the possibility that that fact about the genetic or other predisposition that might be present in those populations could cause us to develop triage algorithms that might cause us to reduce resources available to Black or Hispanic patients because of one of the biases introduced by algorithm development."

The profusion of data since the pandemic began has allowed advanced models to be purpose-built at speed – and has also enabled surprise findings along the way.

Sure, "some of the models that are being built that are labeled AI are really just fancy regression models," said Ross. "But in a way, does it really matter? In any case, [they're] ways to use data in powerful ways to discover new things, ... drive new insights, and to bring advantages to all of us dealing with this disease."

It's notable too that the big datasets needed for AI and machine learning "simply didn't exist in the pre-electronic health record days," he added.

"Just imagine where we would have been if it was a decade ago and we were trying to battle COVID-19 with data that had to be abstracted from paper files, ... manila folders, and a medical records room someplace," said Ross.

"The investments we've made to digitize healthcare have paid off. We've learned that the downstream value of data that's contained in electronic health records systems is incredibly powerful."

Christopher Jason

The eHealth Exchange exchanged over 250 million more patient documents per year following its gateway technology integration.

In the early part of the 21st century, US citizens were in a radically different place regarding patient data privacy concerns and healthcare.

Although patient data security and privacy anxieties remain today, federal agencies and healthcare organizations faced a separate set of fears at the turn of the century, when health technology was first being integrated into care.

“There were definitely some fears,” Jay Nakashima, executive director of eHealth Exchange, said in an interview with EHRIntelligence. “First, there was the fear of healthcare data being broadly breached. Then there was the fear of some sort of an entity out of Washington, DC that maintained a central location housing all patient health information.”

It was out of those fears the Office of the National Coordinator for Health IT (ONC) and the Nationwide Health Information Network (NHIN) conceived of the eHealth Exchange in 2006 to securely exchange patient health data across the country.

Nakashima described the structure of the HIE as a “federal network.”

“A federal network means each healthcare organization needed to create and maintain a pipeline to other healthcare organizations within the eHealth Exchange that it wanted to establish a patient data exchange,” he explained.

By 2009, eHealth Exchange first exchanged data between the Veterans Health Administration (VHA) and Kaiser Permanente. Within two more years, the network added 23 participants and by 2012, The Sequoia Project took the reins and fully supported the eHealth Exchange.

Now, the HIE connects to 75 percent of all US hospitals, over 60 regional or state HIEs, and four government agencies. It also connects 120 million patients across the country.

And most recently, eHealth Exchange can boast progress with its new gateway technology that simplifies connectivity for participants through a single streamlined connection.

“For example, Mayo Clinic has a patient that goes to Stanford in California because they are on vacation or working in the area,” Nakashima explained. “Our job is to create one to five or even 10 direct connections from Mayo Clinic to Stanford Health Care.”

If a health organization creates less than 10 connections, it will not be burdensome or unmanageable for the HIE. However, if a health organization starts to generate hundreds of connections between two health systems, it can become onerous for the HIE and its participants.

“The eHealth Exchange implemented a centralized technology, called gateway technology,” he continued. “It's a single on-ramp or a single connection to the country. Our providers and other healthcare organizations can create one connection to the eHealth Exchange. Then we route their transactions to providers all across the country so they do not have to have a high number of connections.”

eHealth Exchange exchanged roughly 550 million clinical documents using this new structure, which is up almost 300 million transactions from the old format dating about a year and a half ago.

“Data is flowing much more frequently and our customers aren't having to spend as much money on creating and maintaining all of those point-to-point connections,” Nakashima said. “This means they are able to free up significant health IT resources to work on more valuable tasks.”

Furthermore, the new structure helps health organizations expand their national footprint and implement innovative capabilities, such as real-time content quality validation and a national record locator service.

The new approach also helps organizations prepare for regulatory changes, including the ONC interoperability rule and the Trust Exchange Framework and Common Agreement (TEFCA).

Looking forward to 2021, Nakashima said he expects to see more “data pushing,” rather than “data pulling” from health organizations.

This is a more proactive approach to health information exchange, Nakashima explained.

When a patient arrives for her afternoon appointment, her data will already be available at that exact time and place, rather than having to pull the data when the patient arrives at the appointment.

Pulling patient data at the last moment could result in mismatched patient data and potential patient safety issues.

“The vast majority of our participants query every night,” Nakashima said. “An organization will say they have over 100 surgeries and 400 appointments the next day and the system will automatically query the night before, or a couple of hours before an appointment or a surgery, to pull that information and have it available in the EHR system for the clinician.”

Furthermore, eHealth Exchange participants can also set up push notifications to public health agencies across their respective state and county, and even across the country.

“Most participants have their EHR configured to automatically report when, for example, a patient tests positive for Hepatitis B, to automatically push a report to the county and state public health agencies, and then potentially the [Centers for Disease Control and Prevention] CDC.”

Nakashima expects patient data exchange to continue to develop and improve in 2021 and beyond.