Blog

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.





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


Mayo Clinic researchers found that the population health of those under 45 regarding severe COVID-19 infection is greatly affected by chronic disease.


Mayo Clinic researchers have discovered new risk factors impacting the population health of those under 45 when it comes to severe COVID-19 infection. 

Using data from 9,859 COVID-19 infections, researchers found that younger populations had a greater than threefold increased risk of severe infection if they had chronic diseases such as cancer, heart disease, or blood, neurologic, or endocrine disorders.

The team of researchers studied individuals living in a 27-county region of Southeast Minnesota and West Central Wisconsin who were diagnosed with COVID-19 between March and September 2020. The study used the Rochester Epidemiology Project, a linkage of 1.7 million medical records from multiple health care systems that provided significant insight into the risks for the whole geographical region.

"Medical care is really fragmented in our country, so someone diagnosed with COVID-19 at one health care provider might end up at a totally different hospital for their severe case. If those records are not linked together, there's really not a good way for us to understand that that is even the same patient," Jennifer St. Sauver, PhD, a Mayo Clinic epidemiologist and the study's first author, said in a press release.

"By contrast, the Rochester Epidemiology Project allowed us to follow patients from the time they were diagnosed through their use of health care after that diagnosis, even if they were taken care of at different places. In addition, we could look back in their medical records to better understand all of the chronic diseases this population had even before getting diagnosed with COVID-19 and how those diseases might have contributed to more severe infections," Sauver continued.

Researchers identified cancer to be the biggest difference in risk when comparing study participants younger than 45 to those older. For those under 45, cancer was a strong risk factor. However, it was not as significant for the older age group.

Additionally, patients with developmental disorders, personality disorders, schizophrenia, and other psychoses have the highest adjusted risk for severe COVID-19 compared to all chronic conditions.

Researchers also discovered risk factors among races and ethnicities. According to the study, Asian Americans had the highest risk of developing severe COVID-19, followed by Black Americans and Latino populations.

"The Rochester Epidemiology Project allows us to study diseases, such as COVID-19, in a defined population, which provides the ability to translate our results to all people with COVID-19, not just those with the most severe disease requiring medical care," senior author Celine Vachon, PhD, Chair of the Mayo Clinic Division of Epidemiology said.

"This type of infrastructure will allow us to follow patients who had COVID-19 in the 27-county region over time to better understand any future links to disease."




Kat Jercich


For the COVID-19 HotSpotting Score, investigators use a combination of indicators to help predict upticks of cases and potential hospital admissions – with up to six weeks' lead time.


In a study published this week in BMJ Open, Kaiser Permanente researchers put forth a method to predict upcoming COVID-19 surges up to six weeks in advance.  

By examining electronic health record data from Kaiser Permanente in Northern California, the team was able to zero in on ten indicators that, they say, can help effectively forecast an upcoming surge when combined.  

"This current COVID-19 surge has shown us how challenging it is to have a reliable, long-range forecast of COVID’s impact on hospitals," said Dr. Vincent Liu, lead author on the study, in an email to Healthcare IT News.  

"By knitting together diverse streams of health system data, we can identify the earliest signals of renewed COVID activity impacting our patients and contextualize our findings against other prediction tools," said Liu, who is a research scientist with the Kaiser Permanente Division of Research, as well as being a practicing intensivist with the Permanente Medical Group and regional director of hospital advanced analytics for Kaiser Permanente in Northern California.  

WHY IT MATTERS  

Based on 35 million data elements, the investigators ultimately incorporated 10 indicators into "the COVID-19 HotSpotting Score," or CHOTS.

They identified four major indicators:  

  • Patient calls that activated regional "cough and cold" protocols.
  • Patient-initiated "influenza-like illness" email communications.
  • New positive COVID-19 tests. 
  • COVID-19 hospital census numbers. 

The also noted another six minor ones:  

  • Patient calls that activated regional COVID-19 protocols.
  • Respiratory infection visits (routine).
  • Respiratory infection visits (urgent care).
  • COVID-19 visits (routine).
  • COVID-19 visits (urgent care).
  • Respiratory viral testing.  

Although many of the individual indicators signaled an upcoming surge within one to three weeks, the combined CHOTS significantly increased the lead time to as far as six weeks prior to a surge, said the Kaiser team.  

"Over the course of 2020, COVID-19 surprised us at nearly every turn, making longer-term predictions of its impact on our patients, health system, and communities extremely challenging," said Liu in a statement.  

"At the same time, shorter-term predictions – looking only one to three weeks out – left little time to respond adequately,” he said.   

After CHOTS went live in June 2020, the team evaluated it against actual COVID-19 hospital activity through the end of the year.   

"The correlation of the regional CHOTS with hospital census was very strong, peaking with a 28- to 35-day lead time, but with continued correlation when tested out to six weeks," said Kaiser representatives in a press release.  

Researchers note that public health officials and individual health systems could use the forecasting information to help prepare for increased patient numbers – and know when relief is on the way.  

They also flag a few of the study's limitations, such as the fact that the tool's generalizability could vary across settings and geographies.  

In addition, they mention that they developed CHOTS during a time of "great uncertainty" following the first wave of COVID-19 in California.  

"As a result of the extreme urgency to prepare our health system, we depended on clinical judgment and heuristics, in addition to prior health-system influenza patterns, to develop our score," they wrote in the study.

"With the luxury of time, more advanced machine learning or statistical techniques may have produced different calculations," they added.  

THE LARGER TREND  

Given the immense strain on resources that COVID-19 has continued to put on hospitals, multiple teams of researchers have tried to develop predictive tools that can help health systems prepare for the different factors affecting demand – such as length of hospitalization, respiratory failure likelihood, clinical severity and patient outcomes.

More broadly speaking, chief information officers have spoken to the importance of integrated supply chains, which could respond to fluctuations in need.

ON THE RECORD  

"We use machine learning and artificial intelligence every day in our research group to develop predictive models to improve patient care. We applied these tools when we were developing CHOTS, but didn’t find that they improved the tool’s value," said coauthor Patricia Kipnis, principal statistician at Kaiser Permanente's division of research, in a statement.   

"Our research group focuses on pairing the right algorithm with the right use case, and, in this case, a simpler tool showed excellent performance and could be readily implemented and shared," she added.   



Hannah Nelson


With widespread use and training, electronic prior authorization EHR integrations may cut down on clinician burden and increase patient safety.


As healthcare stakeholders investigate ways to leverage health IT to mitigate clinician burden and improve patient safety, the next frontier for the digital health transformation could be streamlining the arduous prior authorization process through electronic prior authorization.

Automating prior authorization could result in higher quality care by cutting back on clinician burden and providing patients with their medications in a more timely manner.

What is electronic prior authorization?

Prior authorization is a utilization management strategy that payers use to ensure patients access the most cost-effective medication available for their clinical needs.

When a drug has prior authorization requirements, providers must submit certain documents to the payer for permission to prescribe the drug. However, the traditional prior authorization process is time-consuming and can lead to delays in patient care.

A 2019 AMA survey found that 64 percent of providers have to wait a full business day to receive prior authorization feedback from payers; 29 percent reported that they had to wait at least three business days.

This delay can lead to patient care setbacks. The survey found that for 91 percent of providers, prior authorizations delayed patient care; 48 percent reported that prior authorizations often or always have this effect.

Delayed prescriptions due to prior authorization can lead to patient safety issues. Nearly a quarter of providers (24 percent) said that a prior authorization-related delay has resulted in an adverse health event for a patient and 16 percent said that the delay led to hospitalization.

What’s more, the arduous prior authorization process places a sizable administrative workload onto clinicians. Almost nine in ten providers (86 percent) reported that the prior authorization burden was high or extremely high, averaging over 14 hours per week to complete 33 prior authorizations.

However, health IT could alleviate some of the clinician burden while also helping patients receive their medications sooner.

Electronic prior authorization (ePA) aims to speed up the process by sending prior authorization documents digitally instead of via phone or fax. ePA can be integrated into EHR systems to allow providers to easily request prior authorization within their clinical workflows.

Is ePA effective?

To better understand how electronic prior authorization might impact patients and providers, America’s Health Insurance Plans (AHIP) launched the Fast Prior Authorization Technology Highway (Fast PATH) initiative in early 2020.

Six payers—Blue Shield of California, Cambia Health Solutions, Cigna, Florida Blue, Humana, and WellCare (now Centene) participated in the project, which ran for approximately 12 months. Availity and Surescripts served as the program’s health IT partners. RTI International evaluated the results as a third party. Point-of-Care Partners acted as an advisor.

After implementing ePA, the total number of prior authorizations jumped by 34 percent. A third of these transactions took two hours or less, compared to before when 24 percent of prior authorizations took two days or longer to fulfill.

More than 60 percent (62 percent) of prior authorizations were electronic after the health IT solution was implemented, and traditional prior authorizations were cut nearly in half. The report authors noted that ePA had little effect on the rate of approvals.

Most providers who used electronic prior authorization had positive feedback. Six in ten providers who used prior authorization regularly said that ePA made it easier to know whether they needed to request prior authorization.

Approximately the same number of providers (57 percent) who were well-versed in prior authorization said that the ePA requirements were easier to understand, and half of them said that the prior authorization decision was easier to view.

Among less experienced providers, the results were less extreme. Less than half (47 percent) said that it was easier to understand if prior authorization was required with ePA, while 43 percent said they did not observe a difference.

Providers who used ePA for most of their patients reported less administrative work related to prior authorizations; 54 percent had fewer prior authorization-related phone calls and 58 percent had fewer faxes related to prior authorization.

However, across the entire provider population, nearly half of clinicians reported no change in the volume or time spent on phone calls and faxes when using ePA.

Overall, seven in ten respondents who used ePA for most of their patients reported that the tool sped up care delivery. Less than three in ten providers said that the amount of time for care delivery was unchanged (27 percent).

Across the entire provider population, 43 percent agreed that ePA increased the speed of care delivery, and nearly half reported no change in care delivery speed (49 percent).

“The review of over 40,000 transactions showed the impact electronic prior authorization makes in health care,” said Denise H. Clayton, research economist of Health Economics and Evaluation at RTI International. “Because clinicians and their staff report more benefits from ePA when they use it more often, greater provider adoption of ePA could help further realize its promise.”

Is there an appetite from providers for this?

About 83 percent of physicians surveyed by Surescripts in 2016 reported that ePA is a top priority, and 64 percent agreed that EHR vendors should provide a service for streamlined prior authorization.

EHR vendors also acknowledged the growing importance of adding electronic prior authorization to their systems; 88 percent of vendors stated that they are aware of the demand for this ePA from their customers. Additionally, about 86 percent of EHR vendors said that ePA is a functionality that customers anticipate the systems to provide.

A June 2020 AHIP survey revealed similar findings; almost 85 percent of payers saw prior authorization automation as a key point of collaboration with providers. Approximately 90 percent of plans said that they were streamlining prior authorization processes for prescription medications (91 percent) and medical services (89 percent), primarily relying on ePA in each scenario.

“Many EHR software systems have incorporated electronic prior authorization capabilities, but the functionality may not yet be a standard option, despite vendor acknowledgment that it can improve clinician workflow and quality of care,” explained Joe Delisle, Surescripts senior business management analyst.

“The inefficiency of manual PA processing translates into hours of wasted time, contributes to workflow inefficiency and impedes a practice’s ability to deliver optimal and timely care,” said Delisle. “The time to enable electronic prior authorization is now.”

ePA Implementation, Use Challenges

Despite the promise of ePA, and an appetite from providers to adopt it, there are some challenges. A study published in JAMA Network Open found that misfiring issues and provider education are keeping ePA EHR integrations from achieving success.

Researchers implemented ePA at a large US healthcare system in two phases in September and November 2018, and used the later-implementing sites as controls.

Using EHR and pharmacy data, the study authors matched epA prescriptions with non-ePA prescriptions based on insurance plan, medication, and site, before and after ePA implementation.

Overall, 64.2 percent of ePA prescriptions (24,930) were filled, compared to 68.8 percent of control prescriptions (26,731), a negligible difference.

The researchers suggested several possible reasons for this result.  

First, ePA fired for less than two percent of prescriptions, which is less than the nationwide average. This suggests some potential misfiring.

There were no substantial differences for commonly used medications for chronic illnesses. However, there were larger gaps in medication adherence for dermatological agents and lifestyle medication for ePA compared to control prescriptions.

The study authors suggested that ePA may have misfired for medications that did not require prior authorization, such as vaccinations, low-cost topical medications, and glucose supplies.

Additionally, since not all healthcare payers have ePA capability, providers may have been using ePA and traditional prior authorization processes simultaneously. In fact, the study authors noted that approximately 75 percent of providers that use ePA leverage several prior authorization solutions.

Next, the authors noted that upon ePA EHR integration, providers may have faced a learning curve that hindered them from using ePA to its fullest capacity. For instance, prior authorization denial in-basket messages may not have been read immediately.

However, the authors noted that over time, these barriers could diminish with use.

Strategies for improved ePA utilization, integration

The JAMA researchers suggested that reducing fragmentation between payers and ePA could reduce the potential misfiring of medications, especially because payer information may not have been up-to-date.

“This may be increasingly possible as integrated delivery networks and risk-bearing contracts with insurers grow, due to focus on the use of technology to improve care coordination,” they explained.

Additionally, integration of data and processing with pharmacies into the EHR may enhance efficiency.

“These findings offer several broader lessons for health information technology interventions, particularly the importance of testing whether the interventions that are supposed to improve care actually do,” the study authors explained.

“Health information technology represents just one type of tool, and, in this case, computerizing the prior authorization process may not have actually addressed the barriers to efficiency, especially when not all payers participate in the technology,” they continued.

The researchers suggested that future studies investigate whether different ePA implementation processes could improve efficiency.

“This research emphasizes the need for rigorous study of these types of interventions not only to inform effectiveness within healthcare systems but evaluate any issues with implementation,” the authors explained.

ePA could benefit both providers and patients, but like many health IT initiatives, true success will only come from widespread use and sufficient provider training.





Hannah Nelson


EHR integrations that include anatomical inventories and gender identity could improve quality of care for transgender and gender diverse patients.


Anatomical inventory and gender identity EHR integrations could help provide gender-affirming care for transgender and gender diverse (TGD) patients by recognizing each patient’s unique gender identify for clinical decision support and population health management, according to a study published in JAMIA.

While some TGD patients undergo gender-affirming interventions such as hormone therapies, hysterectomy, and breast augmentation, others do not undergo any medical or surgical procedures.

For providers to deliver patient-centered care, they must be aware of each patients’ unique gender identity, as well as their anatomy and any surgical procedures they may have undergone.

To keep record of each TGD patients’ medical history, the study authors recommended that hospitals and community health centers integrate anatomical inventories into EHRs. These EHR integrations would allow clinicians to document gender-affirming surgeries and track the presence or absence of specific organs in order to inform preventive health screenings and care plans.

“A clinician who is using an EHR system that does not include an integrated anatomical inventory may be prompted to recommend a Pap test to a transgender man who does not retain a cervix after gender-affirming surgery, because the only information in the EHR system about that patient’s organs may be based on a female sex assigned at birth,” Alex Keuroghlian, MD, MPH, senior author explained in a public statement.

“These are the types of mistakes that can increase mistrust in doctors and the medical system in general among transgender and gender diverse patients and lead to patients simply avoiding care,” continued Keuroghlian, who serves as director of education and training programs at The Fenway Institute. “This contributes to the disparities in health experienced by transgender and gender diverse people. The tools now exist to reduce these mistakes, and hospitals and community health centers should be using them.”

The study authors also suggested that health IT developers implement gender identity, sex assigned at birth, and anatomy data into clinical decision support tools and population health management systems.

“Clinicians use their best judgment based on experience and wisdom to provide quality care, but they also rely on clinical decision support tools derived from information in electronic health records,” noted Chris Grasso, MPH, lead author and associate vice president for informatics and data services at Fenway Health.

“Given the significant disparities in health that transgender and gender diverse people experience in comparison with their cisgender peers, it is incumbent upon health care systems and health information technology vendors, including electronic health records, to improve clinical care for these patients,” Grasso continued.

The study also noted the value anatomical inventories could have on population health management. Healthcare systems could use anatomical inventory data to create internal dashboards for care disparity detection.

For example, the study authors noted that a customized dashboard with anatomical inventory data may show that in the last year, only 60 percent of TGD patients received depression screening in primary care, compared to 85 percent of cisgender women and men. From there, the care team could review records and speak with providers to investigate potential reasons for the disparity.

“Without these customizable dashboards, it can be very difficult to detect disparities in care among patient populations. If disparities exist but cannot be measured, it is often impossible to address them,” noted co-author Hilary Goldhammer, SM.

The study authors also pointed out the importance of data interoperability when treating this population, as TGD patients may access care and services from several sites, such as clinics, hospitals, and pharmacies.

To promote care coordination, the researchers called for organizations, such as Health Level Seven International (HL7), to develop standardized terminology and fields that capture gender identity, sex assigned at birth, name and sex on insurance, name used, pronouns, and the anatomical inventory.

Interoperability of this data would allow providers who are seeing a patient for the first time to address the patient using their correct name and pronouns.

“EHR systems that integrate gender identity and anatomical inventories, and reference those fields and forms to produce clinical recommendations, identify health disparities, and promote culturally responsive communication, will allow for more tailored, gender-affirming, and timely care for patients,” said Julie Thompson, PA-C, co-author and medical director of trans health at Fenway Health.





Hannah Nelson


COVID-19 has advanced the digital health transformation, bringing executives to drive health IT innovation with urgency for business resilience.


Executives are driving health IT innovation after COVID-19 revealed the importance of digital capabilities for business resiliency; 93 percent of healthcare executives said that their organization is “innovating with an urgency and call to action this year” according to Accenture’s 2021 Digital Health Tech Vision report.

Kaveh Safavi, MD, JD, global health lead of Accenture Health, noted that digital capabilities are becoming increasingly vital for effective business strategies.

“We are clearly in this world now where you cannot tell the difference between a business strategy and a technology strategy,” Safavi told EHRIntelligence in an interview. “Our research says nine out of 10 executives basically say that they're inseparable. That is very different than it was a decade ago, where IT was a thing in service of your business strategy.”

“The fact that they're inseparable means increasingly that what choices you make from a technology perspective will actually determine your business strategy and your business capabilities, as opposed to making the business strategies and just going and finding the technologies to get it done,” he continued.

For instance, Safavi noted that every company Accenture works with has migrated much of their technology data center to public cloud.

“Public cloud is considered a relatively elastic way to run a business,” he explained. “You can scale up and scale down much more quickly because you obviously don't have all the physical store and compute capabilities. The cloud is primarily around giving businesses agility to respond either to crisis or opportunity.”

COVID-19 has accelerated the industry-wide shift to the cloud, Safavi noted.

“Care organizations realized that cloud wasn't something you do because it's a good idea, it was something you do as an essential part of having resiliency,” he explained.

Safavi said that many health IT executives are also integrating artificial intelligence (AI) solutions into their organizations.

“AI allows technologies to perform non-routine tasks,” he explained. “One of our challenges in healthcare is that much of what we do is somewhat non-routine and traditional automation only has a certain upper limit.”

“Our care model is now fundamentally based on an interaction between a person who needs something and a professional person who has expertise, and there's a shortage because demand is growing faster than supply,” he said. “We'll never train enough people to close that gap, so we need technology to scale.”

Additionally, Safavi noted that AI can help significantly cut healthcare costs. In fact,
the cost of labor is the single most dominant segment of cost growth inside of healthcare expenditures, he pointed out.

“If you can't figure out how to substitute technology for labor and create productivity, which is something other industries have done to reduce their cost to serve, then you really aren't going to solve the problem,” Safavi stated.

Safavi noted that as the digital health transformation progresses, technology will increasingly become a co-worker.

“We're not talking about technology replacing humans, we're talking about technologies taking tasks, but the people still have to do their tasks,” he clarified.

“As this technology becomes ubiquitously pervasive, it's as normal as the water cooler, and the skills that we need to interact with them are no different than the kinds of social skills we develop to deal with other people in our work,” Safavi said. “We're seeing the same kind of metaphor play itself out with technology.”

When companies integrate new technology, digital literacy is key to technology adoption, Safavi explained. Employees will have to develop different skills and adjust to new organizational cultures.

“For example, increasingly we're seeing the expansion of what's called no-code or low-code technologies,” Safavi said. “What that means is that the normal business user has the ability to go in and change something and get more out of the technology rather than putting a request into IT. That's designed to democratize technology.”

Companies predating the current crop of technologies will have to figure out how to move their data from the existing model to a new model, Safavi explained. However, if you’re a startup, you do not have to deal with data migration from legacy technologies.

“We see a lot of discussions about who's going to be in a better competitive position,” Safavi said. “The incumbents feel somewhat vulnerable or threatened that they know that their technology footprint is not modern, but the cost structure for them to go from the old to the new creates a whole set of problems that new competitors don't have any barriers to start with.”

Safavi concluded that while COVID-19 accelerated the digital health transformation, health IT executives still have work to do.

“It would behoove all of us to recognize that the impact of COVID enforcing the adoption of technologies doesn't necessarily mean that the problems that technology needs to address have been solved,” Safavi said. “This is a very complex issue.”





Alexandros Giannakis and Fabian Gautschi


There are key aspects digital health solutions must meet to positively impact health and quality of life


COVID-19 has fundamentally changed how patients receive medical care. With an almost mandatory need to engage with practitioners through physical distance, a reported 44 percent of cellphone users globally have used their mobile device for a diagnosis or treatment. Precautionary mandates imposed on routine activities have forcefully shifted doctor and patient connections to digital platforms. In response, the rollout of digital health solutions, such as health tracking and management apps, has surged as care providers and patients adopt and adapt to digital engagement services with unprecedented fervor.

Now, while digital health solutions such as health tracking apps are not new ideas, the wide adoption of these tools by practitioners to directly interact with patients alongside quantified holistic management of the patient’s health is. This poses a new opportunity for physicians and health specialists to guide their patients through major health management areas – general health, activity, biomechanics, sleep, nutrition, mental health and omics – and impact quality of life, health status and treatment outcomes.

As new digital health solutions are developed by both healthcare industry insiders and new digital natives, there has been a huge focus on the experience aspect of such offerings. Aiming to reach the standards of consumer goods and retail industry solutions, these digital health tools have achieved progress with user-friendliness, engagement and seamless connectivity. However, a great experience is a founding block to delivering value but remains insufficient to achieve health outcomes. The digital health solutions that positively impact health and quality of life will reflect the following key aspects:

1. Design treatment based on a holistic view of the patient.

One recent study correlates 80-90 percent of health to social determinants (i.e., health behaviors that result from social conditions). Tracking patients’ lifestyle and behavior, together with relevant health factors of general health, activity, biomechanics, sleep, nutrition, mental health and omics are imperative to coaching a patient towards wellness. Consider a diabetes patient who is using a digital diary shared with their physician to track diet. Although the diary will afford the physician line of sight into key treatment factors, such as sugar intake, it leaves room for other disease enablers to fall through the cracks. For example, if physical activity and sleep – two high-risk factors in patients with diabetes that influence energy and dietary uptake – are not similarly monitored, there is a significant risk of treating the patient with a sub-optimal nutritional and insulin treatment regime. Similar applies to monitoring mental health, which is a key determinant of the patient’s willingness and ability to adhere to a disciplined treatment and a balanced lifestyle, both key determinants of long-term health as a diabetic.

These selected examples reflect how digital health solutions must consider the patient’s holistic health to properly contextualize both the disease and the most effective treatment. Every therapeutic area has its own set of relevant factors. Therefore, digital health solutions must be tailor-made in order to capture and analyze the information that is relevant to each therapeutic area and its specific application within individual treatments. As a first step, organizations pursuing digital health solution offerings must define their data strategy by clearly outlining the data required to obtain objective and holistic information about the patient. Only by leveraging the right data, can decisions informed by digital health solutions lead to improved outcomes and better quality of life for patients.

2. Access and analyze data from multiple sources to enable a holistic view of the patient.

While digital health solutions have already increased access to care, their biggest value-add will be unlocked when they can access and analyze patient-related data stored at disparate databases. Expect that these databases will belong to multiple parties including the patients themselves, health insurances, healthcare providers, wearables’ companies, etc. To ensure all this data can be leveraged for the development of meaningful and personalized insights, without having to go through time-consuming, costly, and often restrictive legal technical transformations, digital health solutions must be able to leverage information in situ – where it is located – instead of having to pull it into a centralized location. Practically, this works by enabling analytics across distributed databases without having to overcome limitations like data privacy/ownership, as the raw data is being leveraged without being disclosed or seen by third parties. Going back to our example of a digital diary that tracks a diabetes patient’s diet, this would enable synchronization with the patient’s medical record that includes information on past hyperglycemic events. Using this data can help define which levels of blood glucose can be considered safe by providing health experts a higher level of accuracy of the type of nutrition that will be most effective for the patient.

3. Let the results speak for themselves.

Possessing a holistic view of the patient through the aggregation of data from multiple sources, physicians will be able to put disease treatment into the full context of the patient with whom they are working. Having the right data will allow the care that practitioners provide to be hyper-personalized in a way that cannot be achieved through traditional drug treatments. Leveraging data and insights to guide the body’s natural health defense systems into action will help improve health and treatment outcomes. The improved outcomes should be highlighted throughout the patient’s journey in order to further encourage engagement with the digital health solution and sustain the positive impact.

Ultimately, as more patients successfully improve health and treatment outcomes using digital health solutions and technologies, these offerings will further evolve into delivering an increasing number of standalone treatments, known also as digital therapeutics. To get there, offering great experience can draw patients in, but technically designing these solutions to access and analyze the right holistic data no matter where it is located and to whom it belongs is what will unlock better health outcomes. Care delivery has changed significantly with incredible speed, ensuring these emerging solutions are tailor-made to each therapeutic area and patient will be the impetus for successful treatment.





David Raths


During the Precision Medicine World Conference, informaticians describe the rapid progress they made harnessing EHR data as well as their hopes for improved public health infrastructure


Informatics executives are making a valuable contribution to the pandemic response through the collection, dissemination and analysis of EHR and clinical registry data. During a recent panel discussion, leaders from several health systems also described the challenges and shortcomings they faced and a wish list for the future.

The June 15 Precision Medicine World Conference panel was hosted by Atul Butte, M.D., Ph.D., director of UCSF’s Institute for Computational Health Sciences. He asked his panelists to describe some of their accomplishments during the COVID era, and each respondent described significant changes their teams were able to make in short order.

For instance, Melissa Haendel, Ph.D., chief research informatics officer at the University of Colorado, who leads the National Center for Data to Health, described the number of people who came together to harmonize and aggregate data and create the National COVID Cohort Collaborative (N3C), which aims to take EHR data and harmonize it and bring it together and make it broadly accessible. “One of the overarching goals of this initiative was to create a fully transparent, reproducible, and broadly accessible electronic health record data repository of COVID patients and matched controls being drawn from a variety of different clinical institutions,” she said. “We're up to almost 90 institutions now that have signed on to the initiative. We now have over 2,000 people working on it. It demonstrates the sort of commitment and partnership between the community members, the research networks from the different common data models, the government centers for translational science award sites, and commercial entities all working collectively together in a rather unprecedented governance structure to enable the creation of this limited data set, which to our knowledge is now the largest publicly available, limited data set in U.S. history.”

Philip Payne, founding director of the Institute for Informatics at Washington University in St. Louis, also works on N3C. He described his organization’s work with partners at BJC Healthcare. “The real lesson learned for us was all about how we realign our priorities — how do we harness our capabilities in the informatics and data science research arena, and use them to tackle what was largely an operational problem? These included activities, such as bringing together all of the data across our regional health systems and brokering that data such that it would be available for broad use for hospital capacity planning, response planning, and then later in the pandemic, to help manage our public vaccine campaigns, all while at the same time making sure that we're able to do our core work of research around the pandemic and make that data quickly available to our investigators.”

Payne added that this wasn't just about providing data, but also about pilot funding. “We launched a series of just-in-time pilot funding mechanisms to bring together different disciplinary teams that could tackle fundamental problems such as one project that led to a predictive model to identify patients coming into our emergency departments that would benefit from palliative care as opposed to admission to the ICU, especially for those with multiple comorbid conditions and a high probability of mortality.”

In addition, Chris Longhurst, CIO at UC San Diego Health, and Jessie Tenenbaum, Ph.D., chief data officer for the North Carolina Department of Health and Human Services, described some of the challenges they overcame in tracking and reporting on COVID cases and vaccination rates.

Despite all the impressive work described, Butte asked the panelists what tools they wish they had and how we could best prepare for future challenges, including the next pandemic.

Longhurst noted that UCSD has all its students and employees on the same electronic health record that it uses for its patients. “That really helped to support us during the pandemic, because we had all that data about our testing and our vaccine administration in one place. However, I'd say that the vaccine data on our populations has been a real challenge. The State of California said if you're going to administer COVID vaccines, you need to report it to the registry. But, of course, these registries were built primarily for pediatric immunizations — low volume, and in many cases, not bidirectional interfaces. We really stressed those systems, and the public health infrastructure broke when we stressed them. So there was a period of time for a month or more when they were unable to send us that bidirectional data, and we couldn't integrate that, to understand the first- and second-dose gaps and things of that nature.”

Although they have done a lot of hardening of the system in California, they still have significant infrastructure issues. “For example, here in San Diego County, we have our own county registry that then reports data to the state,” Longhurst said. “And, of course, they use different identifiers so it's very hard to reconcile that. The one perhaps provocative suggestion I might make is that we shouldn't be doing anything at the county level. I don't think that our county IT colleagues really have the wherewithal from a security standpoint or data expertise standpoint. And I think that if we can harden that and centralize it at the state level, at least, that's going to make it easier to get the right data to the county public health leaders, but also make it easier for the health systems, which in most cases, span multiple counties.”

After describing some of the efforts the State of North Carolina has made to automate aspects of public health data reporting on COVID, Tenenbaum noted that although they now have all this data, they have a lot of trouble with linking it. “We have huge efforts right now at creating a master patient index. We don't have one identifier for vaccination data versus case data. And so for breakthrough cases, we're having to do a pretty manual matching probabilistic process. We're working with our state HIE, which is really good at this, to get that master patient index.”

Payne noted that his organization operates a health system that spans multiple states, about a 300-mile catchment area. “We see an extreme degree of variability in the vaccine registries and the ways in which vaccination information is being documented in EHRs, in the occupational health context or in student health contexts relative to our universities, not to mention what happens when we have large public vaccination events run either by the National Guard or various public health departments.” They work with five different public health departments within just the St. Louis metropolitan area.

“The timeliness of that data is very challenging based upon how it's collected and how it's submitted,” Payne stressed. “The way in which we can query it from the registries is highly variable. That leads to some very challenging situations where we're trying to understand what is our real state of vaccination, and in particular, in communities that are highly underserved or at risk. We are a microcosm of the U.S. healthcare system, where we have a lot of access, a lot of equity and a lot of disparity issues to navigate, particularly amplified in this campaign. We can't get to that data quickly enough. And I think it speaks volumes to the lack of a real interoperable healthcare data fabric at a regional or national level, which we all knew was the case. And we've just sort of amplified our understanding of that.”

Haendel closed by noting that a few of the themes that were mentioned included centralization and identifiers. “It's all about being able to harmonize information for asking important questions. In the face of a new disease, we really want to take advantage of all the different data that we can about an individual and a population. In healthcare i we take data that is about a patient. Now we have EHR data, we have imaging data, we have viral sequencing data, we have survey data. We ship those data to different places, never to be reconnected again. And so my hope in the future is that we have a system where we can put the patient back together again — my very technical term — to do the multimodal analytics, get the imaging machine learning working together with the clinical data at the scale of the whole nation. And that really does take that management of centralization into the state or the national level, and really good identifier management for security as well as for data analytics.”





Hannah Nelson


A northeast Indiana health system has joined the statewide HIE to support public health efforts that lean on interoperability and data exchange.


Parkview Health, a 10-hospital health system in northeast Indiana, has joined the Indiana Health Information Exchange (IHIE) to support statewide, data-driven public health efforts powered by interoperability.

IHIE is the non-profit organization that operates the Indiana Network for Patient Care (INPC), the largest inter-organizational clinical data source in the country. INPC has data on more than 17 million patients from over 117 hospitals, 18,000 practices, and 50,000 providers.

“As Indiana’s statewide health information exchange, IHIE believes it has a responsibility to securely gather, analyze, and communicate information in the best interest of public health, and specifically, in support of the Indiana Department of Health,” John Kansky, chief executive officer of IHIE, said in a press release.

IHIE consolidated with Michiana Health Information Network (MHIN) in 2020 to form a statewide HIE, providing healthcare stakeholders with a comprehensive source of connected patient information.

The consolidation has allowed IHIE to accrue clinical data to improve patient care and support public and population health initiatives, the HIE noted.

Now, the HIE’s clinical database will grow even larger with Parkview as a new partner.

“I believe Parkview’s participation will have a significant positive impact, and we greatly appreciate their participation,” Kansky continued.

Ron Double, chief information officer of Parkview Health, noted that COVID-19 has highlighted the importance of secure data exchange for collaboration and innovation.

“We understand the power of data and its impact on the health of our community,” said Ron Double, chief information officer of Parkview Health. “The pandemic demonstrated the importance of securely sharing information and collaborating with agencies across the state. Parkview is looking forward to seeing the impact of this partnership, especially in research and innovation.”

Parkview’s participation will enhance the statewide asset by increasing interoperability for care coordination and public health research efforts, the organizations said.

“Data are essential to us in our work to protect the health and safety of Hoosiers,” said Kristina Box, MD, FACOG, state health commissioner. “Adding Parkview Health to IHIE will greatly enhance our ability to make data-driven, evidence-based decisions for the whole state.”

IHIE has not only supported statewide interoperability efforts, but national ones, too.

Earlier this year, IHIE and five other major health information exchanges (HIEs) formed the Consortium for State and Regional Interoperability (CSRI).

CSRI aims to boost nationwide patient data exchange by progressing patient data exchange initiatives across the country and promoting state-to-state interoperability for providers, health plans, Medicaid programs, and public health departments.

Additionally, CSRI will form data-driven healthcare insights for federal agencies to advise critical policy decisions and increase health IT innovation.

The consortium is made up of IHIE, Chesapeake Regional Information System for our Patients (CRISP), which covers Maryland, District of Columbia, and West Virginia; CyncHealth in Nebraska and Iowa; Health Current of Arizona; Manifest MedEx of California; and Colorado Regional Health Information Organization (CORHIO).

“CSRI is well-positioned to leverage economies of scale on projects that have the potential to move the interoperability needle in a big way,” Morgan Honea, CEO of CORHIO, told EHRIntelligence.com in a February interview. “I am incredibly excited to be a part of this innovative group and look forward to developing and delivering HIT that can help solve significant data problems.”

In the fight against COVID-19, the six HIEs partnered with their local public health departments to enhance data exchange. The HIEs supported test ordering and scheduling with state and county clinics, as well as the development of dashboards for COVID-19 test results, mortality, and hospitalizations. The HIEs also supported contact tracing efforts, COVID-19 alerts, and predictive analytics to identify high-risk patients.





Hannah Nelson


To recover from COVID-19’s financial downturn and improve patient outcomes, healthcare organizations are prioritizing health IT and EHR optimization.


Healthcare organizations are investing in health IT resources and EHR optimization after a year of COVID-19 financial turbulence, according to the 9th annual Health IT Industry Outlook survey conducted by Stoltenberg Consulting Inc.

The survey collected insights from chief information officers (CIOs) or IT directors at a variety of healthcare facilities.

According to the results, EHR optimization is a big-ticket item for most CIOs in 2021. More than half of respondents (59 percent) said that "getting the most out of existing IT purchases, like the EHR system" is their healthcare organization’s biggest financial goal post-COVID-19.

“In a rapidly evolving environment, technology must adapt to the changing needs of healthcare and the changing preferences of consumers more directly involved in their own care journeys,” the researchers wrote.

Approximately one in three CIOs (31 percent) reported that EHR new version upgrades are the top IT spending priority for their healthcare organizations, while one in four industry leaders reported investment in cybersecurity measures as the top spending priority for 2021.

However, despite CIOs reporting greater investment in EHR upgrades, 33 percent of respondents said cybersecurity was their organization’s top mission-critical priority compared to 30 percent who reported EHR upgrades as the top mission-critical priority.  This is likely due to the uptick in healthcare cybersecurity events in 2021, the report authors noted.

Additionally, after a pause in early 2020, healthcare mergers and acquisitions (M&A) are growing in popularity once again, prompting CIO interest in health IT system integration, the survey authors explained. Approximately 20 percent of CIOs reported that IT integration after system consolidation is a mission-critical priority, indicating the need for high-quality IT support.

However, more than half of respondents (55 percent) reported that as they face decreased revenues from COVID-19, budgeting for qualified IT resources is their organization’s most significant operational burden for the second consecutive year.

The researchers said that enabling a mix of flexible and properly skilled staff is key as CIOs seek to lessen administrative burden and control costs.

When IT support teams are well-versed in both the EHR system and the healthcare organization’s cross-organizational workflow and communication practices, they can better tailor processes to maximize efficiency and system utilization, the researchers explained.

“At a time when the digital experience has become a competitive differentiator for hospitals and health systems, many internally operated help desks cannot handle the crush of inquiries coming their way,” the researchers wrote. “Utilizing IT support resources who can easily flex in and out of project area needs is pivotal for nimble response that better optimizes IT spending without draining resource costs or adding on ramp up and training time.”

Additionally, the researchers called for CIOs to apply analytics to end-user support. By doing so, organizations can determine where further investment is needed. For instance, help desk incident analysis helps underscore large-scale workflow or system education difficulties, the researchers said.

“As a clear view into organization-wide EHR use, this is especially helpful during mission critical events, like crisis management, new system go lives or EHR upgrades to detect areas of concern,” the report authors wrote.





Jessica Kent 


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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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