NextGen looking for a new CEO

The board of directors of NextGen Healthcare has created new executive leadership board oversight committees as the ambulatory health IT developer starts looking for a new CEO.

NextGen announced Monday afternoon that Rusty Frantz, who served as president CEO of the company for more than six years, has “agreed to a mutual separation” would step down from those roles, as well as from the NextGen board, effective immediately.

“I’m incredibly proud of all we have accomplished over the past six years,” said Frantz in a statement. “With the talent dedication I have seen firsthacross the organization, I am equally confident in NextGen Healthcare’s continued success.” He added: “This transition enables me to put 100% of my focus on my most important priority – my family.”

An executive leadership committee will lead NextGen Healthcare on an interim basis as the company enlists executive search firm Spencer Stuart to find a new CEO.

The committee will include:

  • Chief Financial Officer James Arnold, Jr.
  • EVP of human resources Donna Greene.
  • Chief Technology Officer David Metcalfe.
  • Newly-hired Chief Growth Strategy Officer Sri Velamoor (once he joins the company next month).

The ELC will work with a new board oversight committee, comprising independent directors Jeff Margolis Craig Barbarosh, non-executive chairman vice chairman of the board, respectively.

“On behalf of the board, we thank Rusty for leading NextGen Healthcare through a successful operational reinvention,” said Margolis. “Today the company benefits from the highest-quality solution offerings in ambulatory healthcare, best-in-class customer service satisfaction levels, an exceptionally engaged team of executives employees who daily live a culture that believes in better. We wish Rusty the best.”

NextGen Healthcare isn’t the only major health IT player to opt for new leadership in recent months, of course. Cerner announced in May that it would part ways with CEO Brent Shafer three years after he’d succeeded the EHR giant’s late founder, Neal Patterson, launch a search for a new chief executive. (Cerner has since eliminated some 500 jobs from its workforce.)

Earlier this month, Healthcare IT News features editor wrote a case study describing how one health system boosted patient provider experience with help from NextGen’s Otto telehealth platform.

In announcing Frantz’s departure, NextGen (NXGN) affirmed its financial outlook for FY 2022, predicting revenue between $574 $584 million non-GAAP earnings per share range between $0.89 $0.95.

“Our confidence is founded in the Company’s remarkable operational capabilities, rich history of delivering strong innovative ambulatory health solutions, solid baseline of organic growth, new customer wins, ongoing profitability strong balance sheet,” said Margolis.

Twitter: @MikeMiliardHITN
Email the writer: [email protected]

Healthcare IT News is a HIMSS publication.

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70-practice Central Ohio Primary Care tells a digital imaging success story

Central Ohio Primary Care is an independent, physician-owned, primary care group with more than 70 practices more than 400 physicians serving more than 400,000 patients. It also has three imaging centers around Columbus, 10% of its physician practices provide in-office imaging capabilities.


COPC needed more advanced radiology communication capabilities to run its practices more efficiently – a system that would grow with the organization.

“Another significant challenge for COPC was moving hard film from office to office or physician to physician,” said Steve Saeger, manager of radiology services at Central Ohio Primary Care. 

“Even with CD-ROM transfer, immediate access remained a top concern. Lastly, paper scheduling was a tough operational issue with not being able to provide timely scheduling electronically – or access results for efficient sharing across providers.”


COPC needed a technology solution to replace the use of imaging CD-ROMs to provide advanced digital capabilities for much more efficient effective imaging operations. Immediate needs required the implementation of a more advanced picture archiving communication system (PACS). COPC needed to better manage communications with secure storage imaging sharing across its practices for its 25,000 annual exams.

“Another critical need for operational efficiencies was the ability to migrate data move away from paper scheduling,” Saeger remarked. “COPC required an enterprise software solution with PACS technology to improve provider patient experiences to meet our patient care excellence standards.

“To support the organization’s continued growth, COPC also required a very stable system, so as to not experience downtime – even with upgrade installations,” he continued. “While upgrades can provide enhanced capabilities practical tools for support that improve patient provider experiences, installation issues or downtime significantly affect clinical operations.”

“Contacts across various health systems practices are valuable in the initial digital transformation planning process; it’s important to learn from those that have experienced similar situations.”

Steve Saeger, Central Ohio Primary Care

In addition to system stability, COPC wanted a vendor to drive advance digital imaging innovation. In COPC’s experience, it is critical that a vendor provide a dedicated support team so when support is needed, technology vendor team members are familiar with COPC’s practice interface, helping more quickly with operations IT staff – even making recommendations to catch issues on the front-end before they become problems, Saeger said.

“Communication is a key component of any vendor relationship, so COPC required strong communications as a primary consideration for technology vendor partners,” he said. “The ability to track details such as support tickets with resolution notes would also help more efficiently resolve issues if they occur again.”


A colleague of Saeger’s recommended Novarad, that began an almost 20-year relationship.

“Through our partnership with Novarad, COPC eliminated its inefficient paper processes cumbersome CD-ROM review of images,” Saeger explained. “One of the substantial benefits of Novarad is that it was founded is still led by a radiologist. This clinical provider perspective keeps products services aligned with the seamless communication capabilities required of modern imaging solutions to support accurate diagnosis ongoing clinical progress monitoring.

“In addition to enhanced operational efficiencies, Novarad’s support was vital in the integration of COPC’s chosen electronic health records Nuance PowerScribe 360, a real-time radiology reporting platform to enable high-quality radiology reports from physician dictation,” he added.

Paper scheduling also was eliminated with the implementation of the Nova RIS scheduling system for improved operational efficiencies. 

Not only did moving away from paper scheduling intuitively improve scheduling speed, Saeger noted, it also allowed COPC to operate with Modality Worklists in the technologies, in turn increasing the efficiencies of the technologists scanning reducing the errors associated with the manual input of patient demographics into modality equipment.

“As a result of these incremental changes, COPC has effectively enhanced its clinical services quality of care created efficiencies across the organization,” he added.


COPC has seen patient volumes increase annually at a rate of 6-7% in part due to broader imaging system capabilities, effective cost containment building interfaces with EHR vendors, Saeger reported.

“The interface process was simple, with reasonable costs,” he said. “COPC team members could make changes mid-stream that improved the results without additional costs. One of the most significant benefits to COPC is that the Novarad team always makes COPC feel like they are their top priority, in addition to the fact that Novarad is always looking out for COPC its team, enabling us to reach the best outcomes efficiency gains possible for our technology needs.”

COPC clinical specialists now can access imaging studies electronically, seamlessly effectively connecting with other healthcare providers, he added. This is especially important in emergencies, such as when a patient is in the ER. Immediate access to imaging studies prevents duplication of imaging – which is safer for patients more cost-effective for both patients systems.

“COPC also is focused on population health initiatives, including cost versus expenses for patients, to deliver the best care options improve the health of the populations they serve,” said Saeger. 

“COPC often uses Novarads’s comparison studies that are immediately accessible through secure web viewing capabilities so radiologists can review provide diagnostic support to specialists – save money time by avoiding the duplication of imaging orders.”


“Make sure you are confident in your vendor selection,” Saeger advised. “Most likely, once you have a vendor in place, you are with them for the long haul, it can be difficult to make a switch.

“Find a vendor that guides the process offers the ability for database building for healthcare complexities, including procedures, CPT codes schedules,” he continued. “A knowledgeable vendor should be able to take a database of information build a program that works. For those that like to be more involved, the more hands-on experience you have in the build, the more you understthe system when you need to adjust or adapt to new circumstances.”

Every minute counts in patient care, so find a vendor that allows one “behind the curtain” so one can fix things quickly when patients need answers, he added.

“Don’t hesitate to dig into the details, be as involved as possible understevery nuance of the system,” he said. “Find a vendor team that welcomes input embraces the opportunity to work together as partners – always improving enhancing services. As true partners, you should have a solid foundation of innovative digital technology – a stable digital environment – so providers can take the very best care of patients without interruption due to technology concerns.”

And as technology continues to evolve, one should expect additional digital innovations cost efficiencies to increase patient provider satisfaction, he added.

“COPC’s goal is to work smarter with patient care through digital transformation is proud of its current success in operational improvements for both patients their business model,” Saeger said. “To help other providers prepare for such a transition, COPC invites organizations that are considering similar digital technology to visit our office so they can see the technology in action, ask questions about successful installations integrations, learn what to expect.

“I often share this advice: Pick a partner that will invest in your organization’s knowledge base for the best possible outcomes,” Saeger concluded. “Contacts across various health systems practices are valuable in the initial digital transformation planning process; it’s important to learn from those that have experienced similar situations.”

Twitter: @SiwickiHealthIT
Email the writer: [email protected]
Healthcare IT News is a HIMSS Media publication.

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New South Wales invests $106M in single EMR system

The government of New South Wales has set aside a total of AU$30.2 billion ($22.8 billion) in its 2021-2022 budget for NSW Health, the state’s ministry of health.

Among budget items under the Health Cluster, an initiative to unify NSW’s present EMR solutions is getting $141 million ($106.3 million) “to enhance care coordination, further digitisation, improve patient experience increase service sustainability”.


The initiative refers to the Single Digital Patient Record (SDPR) system project, which envisions a “single, holistic, statewide view of every patient – for that information to be readily accessible to anyone involved in the patient’s care”, according to Dr Zoran Bolevich, chief information officer of NSW Health.

NSW Health said in December that the SDPR will consolidate the geographically fragmented health record systems in the state, including the Patient Administration System (PAS), the Electronic Medical Record (eMR) the Laboratory Information Management System (LIMS), into a unified platform.

The single EMR platform will help clinicians get “better informed”, while patients will have a “more seamless” care experience. “It will give patients the confidence that regardless of where they live or which service they attend, their information will be available to their treating clinician in its entirety,” the statement read.

In addition, the first phase of the SDPR project will also get funding under the AU$2.1 billion Digital Restart Fund, said Minister for Digital Victor Dominello in a separate statement on Tuesday.


It was in 2019 when eHealth NSW, the digital health arm of NSW Health, first sought from the market a solution to improve the healthcare system’s EMR, which is one of the strategies under the state’s decade-long digital health plan launched in 2016.

By October last year, eHealth NSW released its Expression of Interest for the SDPR project to shortlist suppliers.

In other regional news, New Zealhas allocated up to NZ$400 million ($289.4 million) to implement its health sector data digital infrastructure over the next four years, including NZ$385 million ($279 million) for the development rollout of Hira, its new national health information platform.

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AI deployments accelerating across an array of complex use cases

Coming through the pandemic, healthcare organizations are ramping up their use of digital technology as they redefine healthcare delivery. The rapid adoption of telehealth in crisis demonstrates their ability to go further.

There have been accelerated shifts toward other emerging healthcare models, along with the investments technologies they require. One quickly growing technology in healthcare is artificial intelligence.

Time-pressured decisions have highly consequential outcomes on a minute-by-minute basis. Using rules-based systems machine learning algorithms, automation can facilitate process execution, drawing on specific patient histories to improve treatment.

By applying AI to these functions, organizations can expedite prior authorization, identify fraud waste, automate billing, coding patient scheduling.

Sashi Padarthy is assistant vice president at Cognizant Healthcare Consulting, leads digital strategy transformation services. Healthcare IT News sat down with him to get his expert views on these aforementioned subjects on the accelerated use of AI in healthcare overall.

Q. How has the use of artificial intelligence in healthcare been accelerated in recent years, including by COVID-19?

A. AI as a technology its overall adoption have significantly advanced over the last few years will continue to accelerate. In the last 15 months we have seen computational biology coming to the forefront.

To be fair, computational biology has been rapidly developing over the last decade as the healthcare industry has gotten access to large datasets, more advanced analytical capabilities, modernized data ecosystems, enabling us to conduct faster more efficient drug discovery research to create precision drugs treatments. COVID-19 vaccinations (Pfizer Moderna) are clear examples of that.

As the medical community has seen the capabilities of AI to help drive this research shorten the amount of time it takes to develop new treatments, the trust reliance on AI is growing.

Computational medicine – the use of AI, machine learning other technologies for early detection diagnosis of disease – has been in the works for nearly a decade. The next milestone is to bring computational medicine to the bedside to be able to identify patient-specific treatments drugs provide them to the bedside clinician in almost real time to significantly enhance patient care.

AI as a technology has advanced in three ways: 1) pattern recognition (computer vision), 2) natural language understanding, 3) natural language generation.

With these advances, a variety of healthcare challenges can be addressed. Here are some examples:

More than 150,000 deaths in the U.S. are related to lung cancer, making it one of the leading causes of death. There are now deep learning algorithms that can detect as well as, or sometimes better than, a radiologist can.

Provider burnout the desire to remove some of the administrative burden has led to clinicians embracing AI. Many providers are now using AI to assist them in creating clinical documentation. AI listens to a patient doctor’s conversation creates a clinical document, which the doctor reviews edits before signing off the chart. This saves clinicians a lot of time increases the accuracy of documentation.

Finally, clinicians are being asked to integrate a tremendous ever-growing amount of data from various EHRs patient-generated data into clinical practices. Until recently we really haven’t had the tools to harness that vast quantity of information in a meaningful way to help patients. AI helps solve that problem. We are selectively using AI now to forecast the spread of different flu strains other contagious diseases a week in advance, with more than 90% accuracy.

Even though AI promises many benefits, we still need to ensure there isn’t any algorithmic bias. Bias is not new in the industry or in healthcare, but, because of its ability to scale, AI can amplify the impact of bias. Therefore the application of AI in a clinical setting will require significant clinical trials to create an evidence base physician buy-in.

Q. Time-pressured decisions have serious outcomes on a minute-by-minute basis. What are a couple of examples of these decisions in healthcare where AI can help?

A. AI has been proven for many use cases is able to assist clinicians in making critical patient-care decisions. AI is not replacing the clinician, nor is it making the decision for the clinician. AI is generating insights for clinicians from data sources traditionally unavailable to a provider at the point of care.

One such example is the use of an AI algorithm to predict psychiatric diagnoses using data from Facebook. The AI algorithm was able to predict psychiatric diagnosis comparable to that of a standard clinical PHQ-9 survey given to a patient to assist the clinician in diagnosing, quantifying or monitoring symptoms severity of symptoms of depression.

The significance being that the questionnaire may have high false-positive rates in primary care settings. Specifically, one meta-analysis found that only 50% of patients screening positive had major depression (Levis 2019). The algorithm, however, has access to large volumes of data that may span days, months years, is objective in its analysis.

Another example: Vocal biomarkers for prediction of psychiatric diagnosis – another AI-driven diagnostic tool for clinicians that can be used by the patient to track analyze over longer periods of time to aid in diagnosing or assessing the severity or change in symptoms of depression.

Access to data outside the EHR, coupled with EHR claims data, are more traditionally available. They are shining a light on use cases that allow the prediction of disease risk, as opposed to diagnosis of an active disease process.

By leveraging AI to analyze larger datasets that include both clinical social data, clinicians can predict a patient’s risk of developing specific conditions, disease processes or suffering a major medical event. It also allows clinicians to develop a patient-specific treatment plan based on the specific health disparities that patients face.

AI algorithms can provide insights to the clinician alerting them that a patient has an elevated risk of developing cardiovascular disease. The algorithms can also provide insight into the challenges the patient faces in mitigating their risk, such as the walkability of their physical environment, access to healthy foods or the quality of care within the geographical area available to the patient.

With added insights into the challenges a patient faces, the provider can work proactively with the patient to determine the most appropriate treatment plan for that individual patient in order to mitigate the patient’s risk factors. Limited to just the information available within the EHR, providers are not able to garner the same level of insights that allow them to provide whole-person care.

Another use-case for AI enabling providers to make faster better-informed decisions is supporting the decision-making process for medically or surgically complex patients. By using deep learning AI machine learning, a provider could weigh the risks benefits of treatment options.

Patients with complex care needs typically have a long medical history, multiple diagnoses multiple comorbidities, which make synthesizing all the information determining a treatment plan based on the best possible outcome difficult time-consuming. A provider could save time determine a better statistical analysis of the risk or benefit of a treatment option that is based on the unique history, diagnosis comorbidities of an individual patient.

AI brings more information to a provider in real time to assist with making difficult complex medical decisions. Providers can leverage key insights at the point of care from multiple data points that are not traditionally available to help patients achieve better outcomes.

Q. Using rules-based systems machine learning algorithms, automation can enable process execution, drawing on patient histories to improve care. Would you elaborate?

A. AI provides the ability to scan across spaces places of care to identify the information that is most relevant to a provider at any given time. Many patients see multiple providers prior to getting to the correct specialist for a specific medical problem. This means that their care the documentation of that care may exist across various clinics, hospitals or health systems.

It is challenging time-intensive for a specialist to have to review various encounter notes, diagnostic testing results other documentation to help them care for the patient. AI can remove that burden learn to identify the specific types of information that a particular provider searches for uses help develop a perspective about the patient based on information from multiple sources.

An example: Cognizant’s Cognitive Computing Data Sciences Lab tackled the challenge of diagnosing diabetic retinopathy (DR) for patients in India who did not have coverage or access to quality eye care. Cognizant partnered with a Bangalore-based clinic, Vittala International Institute of Ophthalmology, to help patients who did not have access to quality equipment specialists.

Cognizant VIIO developed deep learning algorithms that could identify DR 90% of the time, even in low- to poor-quality images. Clinicians upload images the software uses the deep learning algorithm to identify DR. This provides increased access because patients no longer must travel to see a specialist or pay the additional cost of seeing a specialist.

With new models of care coming to the market the tendency for healthcare consumers to shop around, patients will be receiving care across multiple spaces places of care. AI can help create a more unified, seamless experience for the provider patient.

Q. By applying AI to healthcare clinical business functions, provider organizations can expedite prior authorization, identify fraud waste, automate billing, coding patient scheduling. Please explain how this can be done with AI.

A. AI can remove some of the administrative burden surrounding prior authorization, billing coding. AI is better at identifying patterns than a human being. Where a rules-based engine requires updating changes to maintain accuracy, AI learns can get smarter more efficient at recognizing patterns for billing fraud.

AI can not only identify the patterns of fraud faster, but can also help to prevent it. It is capable of sorting through much larger amounts of data identifying patterns of upcoding, whether appropriate documentation exists for a service a patient was billed for other things that seem out of place. 

Like using AI in clinical care clinical decision-making, it is not a replacement for a comprehensive fraud detection program, but a tool to alert a team sooner to patterns that seem out of place.

Natural language processing natural language understanding are being used to assist clinicians with generating clinical documentation by listening to their interaction with a patient turning what it heard into a clinical note. Using this type of AI, a more complete more comprehensive document in a narrative style is created for the provider.

These clinical documents created using NLP NLU are able to show the thought process of the provider more clearly better demonstrate the medical decision-making process a provider went through. 

With CMS finalizing the 2021 Physician Fee Schedule with updated E/M codes, this will better enable providers to code bill for the additional time spent with more involved medical decision-making for complex patients.

AI can remove some of the administrative burden of documenting the necessary information to show the complexity of the patient’s problems medical decision-making for the new billing coding requirements. This also leads to better coding, fewer denials fewer rejected claims.

Like using AI across spaces places of care to help providers gather the most pertinent information, it can also be used to harvest the appropriate information for prior authorizations allow for automatic approvals. This removes the burden on the provider or their staff to have to manually fill out or input the information for prior authorization.

AI-enabled scheduling software can make both a patient’s the medical team’s lives much easier. AI is able to determine the scheduling preferences of a patient by analyzing their past scheduling patterns either auto-schedule or suggest the most appropriate date time, location, provider. 

It can also help to create an optimized schedule for a patient who is seeing multiple providers or receiving multiple treatments in the same day. This reduces the amount of time spent by both the patient the scheduling staff to get the next appointment or series of appointments created.

AI is also able to recognize the complexity of a patient can be used to determine the appropriate visit length for the next appointment. A standard wellness visit for a patient with multiple diagnosis takes longer than one for a patient who has continually been healthy. 

A provider’s schedule can be optimized with this approach because the time allocated to each patient is more customized based on their needs [it] allows the provider to spend the necessary time with each patient without feeling rushed or falling behind in their clinic schedule.

AI is able to make processes that are task-heavy or time-consuming for humans much easier by reducing the overload.

Twitter: @SiwickiHealthIT
Email the writer: [email protected]
Healthcare IT News is a HIMSS Media publication.

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Research suggests Epic Sepsis Model is lacking in predictive power

A new study in JAMA Internal Medicine found that a sepsis prediction model included as part of Epic’s electronic health record may poorly predict sepsis.

Using retrospective data, University of Michigan Medical School researchers found that the predictor did not identify two-thirds of sepsis patients.  

“In this external validation study, we found the ESM to have poor discrimination calibration in predicting the onset of sepsis at the hospitalization level,” UM researchers wrote.   

Epic disputed the study’s findings, saying that the authors used a hypothetical approach that did not take into account the analysis required tuning that needs to occur prior to real-world deployment to get optimal results.  

“In their hypothetical configuration, the authors picked a low threshold value that would be appropriate for a rapid response team that wants to cast a wide net to assess more patients,” said a statement provided by the company.  

“A higher threshold value, reducing false positives, would be appropriate for attending physicians nurses,” it continued.  


As the researchers note, early detection treatment of sepsis have been associated with less mortality in hospitalized patients.

One of the most widely implemented early warning systems for sepsis in U.S. hospitals is the ESM, a penalized logistic regression model included in Epic’s EHR.   

Although Epic developed validated the model based on data from 405,000 patient encounters, the researchers raised concerns about its opacity as a proprietary model.  

“An improved understanding of how well the ESM performs has the potential to inform care for the several hundred thouspatients hospitalized for sepsis in the U.S. each year,” wrote the researchers.

Using the data of all patients older than 18 admitted to Michigan Medicine between December 6, 2018, October 20, 2019, researchers found that sepsis occurred in 7% of the hospitalizations. The ESM had a hospitalization-level operating characteristic curve, or AUC, of 0.63 – “substantially worse,” than that reported by Epic, they said.

When alerting at a score threshold of 6 or higher, which is within Epic’s recommended range, the model identified only 7% of patients with sepsis who were missed by a clinician.  

It did not identify two-thirds of patients with sepsis – despite generating alerts on 18% of all hospitalized patients, creating a large burden of alert fatigue.  

In its statement, Epic argued that the purpose of the model is to identify harder-to-recognize patients who otherwise might have been missed. It pointed to previous research that found the model could accurately predict sepsis, said customers have “complete transparency” into the model.  

According to Epic: “Each health system needs to set thresholds to balance false negatives against false positives for each type of user. When set to reduce false positives, it may miss some patients who will become septic. If set to reduce false negatives, it will catch more septic patients, however it will require extra work from the health system, because it will also catch some patients who are deteriorating, but not becoming septic.  

“In the example given in this paper, if the Epic model was used in real time, it would likely have identified 183 patients who otherwise might have been missed,” the statement added.  


Health systems have increasingly turned to machine learning predictive analytics to detect sepsis in patients in an effort to decrease mortality.  

In 2019, researchers from Geisinger IBM developed a new predictive algorithm to detect sepsis risk, aimed at helping clinicians create a more personal care plan for at-risk patients.  

But the JAMA study reiterates that models have their own challenges, such as alert fatigue or, conversely, defaulting to computer-generated assessments as infallible.  


“Medical professional organizations constructing national guidelines should be cognizant of the broad use of these algorithms make formal recommendations about their use,” wrote researchers.


Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Email: [email protected]
Healthcare IT News is a HIMSS Media publication.

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