Blog

Mike Miliard 


The CIO of Arizona's Health Current describes the health information exchange's efforts to serve its participants during the COVID-19 crisis – and discusses its ongoing efforts to boost data quality and consistency across the state.

Phoenix-based Health Current, Arizona's statewide health information exchange, serves more than 500 organizations: ACOs, behavioral health and community providers, emergency medical services, rural health clinics, hospitals and health systems, payers, labs, LTPAC organizations, and state and local health agencies.

For some time now, the HIE has been engaged with a major initiative focused on data governance and data-quality improvement, working to integrate more clinical and behavioral data across its network. Having seen 700% growth over the past three years, with its participants becoming ever more diverse, Health Current has been grappling with big challenges related to the sprawl and diversity of coding terminologies and data content across its stakeholders.

Since 2017, it has worked across its community of data suppliers and end users to work toward more commonality and uniformity in how data is relayed. That approach has been helpful in recent weeks as Health Current has had to pivot to focus on marshalling healthcare-data resources across Arizona in response to the COVID-19 crisis.

The HIE is working to ensure its data centers and IT infrastructure are solid and backed by multiple redundancies, even as its team members are observing social distancing by working remotely. The aim is to ensure all participants have easy access to critical data without any drop in service levels.

Health Current is enabling widespread access to longitudinal patient medical histories and data from patient encounters during the pandemic via its secure online portal, and with tens of thousands of alerts sent to clinicians and other healthcare staff. It's also offering expanded services for members to help monitor vulnerable populations, and prioritizing recruitment of new participants to expand its range of customers.

Healthcare IT News spoke recently with Keith Parker, Chief Information Officer at Health Current, to see how the HIE is serving as a critical public health infrastructure.

Q. How has Health Current pivoted in recent weeks, in light of the COVID-19 pandemic, to better serve the healthcare organizations across Arizona that rely on the HIE?

A. We're reaching out to our stakeholders and communicating with them about how best to use our data and the infrastructure we've put in place here in Arizona: everything from data-mining our database for our state or other authorized individuals to use for hot-spotting, to using our alert mechanisms, so when different labs or different high-risk patients come across, we can make sure that their care teams and their providers are aware of it as well.

Q. What is the size and scope of Health Current?

A. We don't really frame it in the context of the number of providers, but instead look at it through market-segment penetration. So we have about 98% of our acute care facilities connected and sending information bidirectionally – sending and receiving information.

Q. So the HIE is serving as a pretty critical information backbone to kind of help coordinate response to this crisis statewide.

A. We do have an infrastructure that is a pretty critical backbone for the state, as far as alerting and getting data to distribute across our healthcare community. And we send out, from a broad message perspective, well north of 20 million messages a month. But from a raw perspective, one encounter could equal several different message types that are going back and forth.

Q. That's just on a typical day?

A. That's typical.

Q. So you expect this to ramp up as the crisis continues to unfold.

A. Yes, and we've got the infrastructure in place. We've been doing north of 10 million alerts for the past almost year now.

Q. Talk about the importance of data quality.

A. We're on a journey to improving the data quality here in Arizona from a systems approach: How do we actually put in place mechanisms that engage our stakeholders, as well as the infrastructure that we have currently, to ensure that we have quality across the data continuum.

And what I mean by that is the data has to be input directly from the data source to us: The connection has to be built correctly – and to be built correctly as far as the types of data – and from a validation perspective: Is it actually syntactically correct, coming across? Is it complete and is correct?

We started down this path a little over a year ago, and I've got to tell you, it still feels like we're just at the beginning of that journey. There's just so much work to do to be able to clean up the data, make sure it's accurate, make sure that it's coming across on a regular basis.

Q. How do you approach that work?

A. We take an approach that's more collaborative. We have a data governance council. A lot of our ideas go to them. We put in place standards that go through our data governance council that we then take to our board, which is a representation of Arizona's market: payers, hospitals, providers, behavioral health, physical health. And then we move forward with it.

So from a data-governance perspective, that collaborative approach that promotes getting in the weeds, we actually have started running data-quality reports back on a subset of our facilities to say, "In these segments, this is what we're seeing." We've identified that we're missing specific maps, because not everything meets national terminology. And some of the areas that we've had to do this in his round of facility naming conventions, because what they name their facilities is not going to be in any national terminology book.

Q. And what about in a situation like the pandemic, which is so fast-evolving, with new CPT codes, new ICD-10 codes, new terminologies in general?

A. That's where having the foundation in place is valuable. We already know what those codes are, so we're just going in and validating that we're receiving those codes that we can data-mine against those codes. So when you take a look at it, it's the CPT codes, it's the LOINC codes for the labs, it's the CDC guidelines for symptoms or other areas that they're looking at.

So we've pretty quickly developed what that compendium looks like, and then we could go in and validate. But part of what we've done is, when we bring in our large data sources right now, those 98% of our hospitals, our acute care facilities that are sending us information includes labs. We've already got the foundation in place to be able to move that information back and forth. So it's just a matter of us going in and validating: This is the appropriate coding that we're looking for.

And then we can connect with the data sources and say, yes, that's how they're settling in as well. When it comes down to the national coding elements, it actually makes it a little easier because from a LOINC perspective, they follow those guidelines.

And if they don't, we can identify it. Then we can talk to them as far as how they actually code it. And then we have the appropriate data maps in place to be able to bring the data across as usable data. So the foundation really lends itself to us being able to stand up pretty quickly, to be able to say, as the testing is being distributed, we can then already have mechanisms that we're wrapping up or putting in place to be able to alert on those tests.

Having the right pipes in place is key – having those pipes support national standards, and having the appropriate mapping in place so we can make that data usable, and then doing spot checks (Is it actually working?), and validating against it. So from that perspective, it makes it a lot easier when something like this does arise to be able to work with our stakeholder and say, "How can we support you better?"

Q. Do you get the sense that the stakeholders, generally speaking, are appreciative of the role you're playing here and are using you to the fullest potential?

A. I would say here in Arizona, yes. So, we've got a pretty collaborative state when it comes to working together and sharing the information. We try to hit 90% or better on the data quality coming across. But the different data types is going to lead to natural variation that's going to be in the data coming across.

And our data sources work hand in hand with us to make sure the data quality is there. And then the end users: how is the data actually going to be used? A lot of our data goes out to those care teams, those care navigators that are supporting the rest of the care teams, the providers, the hospitals, the outpatient facilities on how to better manage those patients. The 10 million alerts that we send out are because most of our complex patients are on multiple provider panels.

Q. In recent years, as interoperability and data exchange imperatives have evolved, many HIEs have similarly innovated the services they provide. Talk a bit about how Health Current has changed its own business model in recent years.

A. Like evidence-based medicine, we pursue an evidence-based strategy. When we started sending out alerts we were at maybe 10,000, we thought we're doing pretty good. But when we reached out to our stakeholders and actually had deep conversations as part of what they really needed with the individuals who are using the alerts, we went from less than 100,000 to over 10 million a month.

We saw individuals starting to update their panels, stay more in touch, so it's really listening to our stakeholders. We do that. We take that same approach when it comes to data use from, say, pop health or analytic platforms. So we've spent a lot of time and efforts on developing different queries that we hit against our system to be able to support specific measures.

So if Organization A is using a given analytics platform, instead of just giving them CCDs, we give them the specific national codesets that they're looking for for those. Along with specified demographics, it's a lot cleaner. It goes into their system a lot easier. We do the normalization on our end, so it just feeds right into their environment.

We also work on it probably hitting their system in a few cases where we can support them with the alerts, to where it can support a care pathway. So they're using IP or solutions out there to be able to say, "I've got a diabetic; he presents in this way; this is the recommended pathway for the care team," they need that information to hit the system.

And we provide a mechanism when it's done to be able to hit their system like that. And we try to do it from a real-time perspective, so that information comes across. We immediately set it off to their system. The coding is then in place for them to be able to run whatever algorithm is downstream with their partners, with their care teams, as well.

So it's really listening to our stakeholders and understanding the direction they're going. It's also looking at it from an integrative perspective. We've the approach to where we're not shying away from 42 CFR information, if we bring in substance abuse information and make it available in accordance with SAMHSA, with national direction on that as well.

So it's really listening. As our environment's going to integrated facilities, integrated care, it's making sure that we have a data infrastructure or a direction that we're moving in that can support that as well. So it really goes from HIE to data management. So from the exchange to how do we actually manage data across market segments, and aligning both state and federal guidelines.

Q. We've seen often how in times of natural disaster, whether it's wildfires or hurricanes, HIEs really serve as key infrastructure: a "public utility … as critical as having roads, as having fire hydrants, as having an electricity backbone," as another HIE director once told me. Is that how Health Current sees itself as the COVID-19 crisis unfolds?

A. We see ourselves as a partner here in Arizona to make sure that we're providing the best care possible. So we see ourselves definitely as a piece of that puzzle. But only a piece of the puzzle. In hurricanes, when records were lost, well, you've got at least the start of a longitudinal record inside the HIE that you can begin using. And as the data elements become more complete and the quality improves, that only gets better over time. But yes, we definitely see ourselves as an integral part of the healthcare system and providing better care.





Heather Landi


Health IT leaders at hospitals and health systems are fast-tracking major technology projects—some in a matter of days.

As coronavirus cases rapidly increase in the U.S., healthcare chief information officers (CIOs) and IT executives are facing an unprecedented situation with a demand to ramp up technology tools on multiple fronts.

Hospital CIOs are quickly putting up telehealth infrastructure and telecommuting capabilities for thousands of employees and also developing screening chatbots and tracking tools to help frontline healthcare workers respond to the coronavirus pandemic.

"The leaders out in the field, at clinics, nursing homes, and hospitals, they are working at a pace that is heroic at best. What they are doing is right now is pretty amazing," said Russell Branzell, president and CEO of the College of Healthcare Information Management Executives (CHIME).

"We’ve never experienced anything like this," Geisinger Health System CIO John Kravtiz told FierceHealthcare, noting that IT teams are working at "lightning speed" to support clinicians. "You get things done, you plan on the fly. We're providing resources to solve problems. We have a fabulous IT team here at Geisinger. I can’t believe what we’re doing."

One of the key ways that technology can help in the response to COVID-19 is to reduce exposure from person-to-person contact and to prevent hospitals from being overrun.

Geisinger has developed a chatbot to help triage and screen patients remotely and is setting up video chat capabilities for patients admitted to the hospital to connect with their families at home. The health system also is using existing tools such as e-ICU to manage patients across its campuses.

Across its service area, Geisinger also has set up 13 screening tents outside of its facilities to screen and test patients. "The screening tents are like a MASH unit, there are computers and printers out there and they are fiber-optic connected. It's amazing how fast we were able to turn those things around," Kravitz said.

Branzell, a former healthcare CIO, said IT leaders are quickly shifting from focusing on the day-to-day IT needs inside the hospitals to enabling community-wide integration, including home wireless and internet connectivity. 

"What I'm hearing across the board is that organizations are making this stuff happen in days that could have taken years," he said. 

Virtual visits and telecommute

As Pennsylvania has joined California, New York, and Illinois with putting restrictions in place to curb the spread, health systems in that state are rapidly setting up telecommute capabilities and enabling physicians to do virtual visits from their homes.

The University of Pittsburgh Medical Center (UPMC), which operates 40 hospitals, has pivoted its patient-facing telehealth services to focus on onboarding primary care physicians to address the flood of patients with potential COVID-19 symptoms.

"We're also recognizing that many of our healthcare providers may be potentially quarantined or may be COVID-19 positive and this enables them to deliver telehealth care from their homes. We have been focused on making sure they have appropriate technology at home," Robert Bart, M.D., chief medical information officer at UPMC, told FierceHealthcare.

The health system's IT network is currently supporting 30,000 concurrent connections, with about 18,000 to 20,000 of those remote users, Bart said.

UPMC's urgent care telehealth platform, AnywhereCare, has seen a six-fold increase in visits, from an average of 80 visits a day to 500 visits a day. The health system's ambulatory care telehealth platform saw visit volume in one 48-hour period equal to the telemedicine visits performed in all of 2019, according to Bart.

"On our peak day, we saw about 1,500 visits. That number is climbing and we expect it to go higher," he said.

The health system also is working to deliver telemedicine functionality to all its inpatient units and ICUs.

Bart said UPMC's IT infrastructure is robust and has the capacity to scale up to meet ongoing demand. "We're less concerned with infrastructure and hardware than the durability of the people to deliver the care," he said.

Geisinger is working to onboard 1,000 physicians for virtual care visits by providing devices, cameras and headsets to physicians at their homes, according to Kravitz.

Danville, Pennsylvania-based Geisinger Health System services over 3 million patients in 45 counties in areas of Pennsylvania and southern New Jersey.

The IT department also is working to support radiologists working from home who need significant technology resources including high internet bandwidth, high-resolution monitors, and voice-to-text capabilities to transcribe documentation and get it back into the health systems' electronic health record (EHR), Kravitz said.

The number of Geisinger staff members and physicians working from home has doubled compared to a weather-related emergency such as a snowstorm, from about 6,000 concurrent users to 13,000 users, he said.

Crisis driving innovation

As health systems respond to the pandemic, IT leaders are pushing forward innovative technology solutions. Developers are working on tools using Fast Healthcare Interoperability Resources (FHIR) APIs to share public health data, Branzell noted.

Geisinger has worked with its local health information exchange, Keystone HIE, to develop a "heat map" dashboard that pulls in data from the Department of Health and laboratories and provides real-time data on people reporting symptoms and coronavirus cases by county. Hospital emergency departments find that information valuable to better prepare for potential patients coming in, Kravitz said.

IT leaders are setting up these capabilities while also ensuring that systems are running at peak performance and maintaining strong cyber defenses. "Cybersecurity criminals will look for vulnerabilities and take advantage. The cyber-surveillance cannot stop or we run the risk of being attacked and having major problems on our hands in our crisis situation," Kravitz noted.

UPMC is taking steps to implement telemedicine capabilities on EMS ambulances. "If there is a potentially affected patient, we can bring the physician to the patient to decide whether a patient needs to come into the ED or not. That will be helpful to triage patients in near real-time and potentially allow patients to stay in isolation without the risk of exposure to other individuals," Bart said.

Branzell predicts that the technology advances occurring now won't reverse once the pandemic ends.

"With telemedicine and remote monitoring, this is the new norm and how we provide care going forward is going to fundamentally change," he said.





Joanne Finnegan


It may be time to add a fifth “vital sign” when physicians and other clinicians evaluate patients: their travel history.

Asking about travel history when evaluating patients could help to prevent the spread of novel coronavirus and manage any future pandemics, two infectious disease doctors wrote in a commentary in the Annals of Internal Medicine.

Typically, clinicians assess patients’ vital signs when evaluating their health—temperature, heart rate, respiratory rate, and blood pressure.

“Given the increasing frequency of emerging infectious diseases that are geographically linked, is it time to add a ‘fifth vital sign’?” wrote the authors, Trish M. Perl, M.D., chief of the division of infectious diseases and geographic medicine at the University of Texas Southwestern Medical Center, and Connie Savor Price, M.D., chief medical officer at Denver Health and a professor in the division of infectious diseases at the University of Colorado School of Medicine.

That fifth vital sign could help to prevent the spread of geographically linked emerging infectious diseases such as coronavirus, which has been officially named COVID-19.

“The current outbreak is an opportune time to consider adding travel history to the routine. The COVID outbreak is clearly moving at a tremendous pace, with new clusters appearing daily,” said Perl, in a university announcement. “This pace is a signal to us that it is a matter of time before we will see more of these infections in the U.S. What is different with this outbreak is that this virus is more fit and transmissible and hence there has been much more transmission.”

While the numbers are changing daily, in the U.S., there are now more than 100 confirmed cases of coronavirus in 15 states and six deaths linked to the virus.

The infectious disease doctors said a simple, targeted travel history can help put infectious symptoms in context for physicians and caregiver teams, and then trigger a more detailed history, further testing and rapid implementation of protective measures. The added vital sign could signal a lurking communicable infection and flag potential risks to healthcare personnel and other patients.

Shared electronic health records also can integrate travel history with computerized decision-making support to suggest specific diagnoses in recent travelers, the authors said.

“We have the infrastructure to do this easily with the electronic medical record, we just need to implement it in a way to make it useful to the care teams,” said Perl. “Once the infrastructure is built, we’ll also need to communicate what is called ‘situational awareness’ to ensure that providers know what geographic areas have infections so that they can act accordingly.”

COVID-19 began in China and has continued to spread to more countries. Epidemics in Iran, Italy, and South Korea have shown no signs of slowing.

In fact, when the early coronavirus outbreak was concentrated in China, Anthony Fauci, M.D., director of the National Institute of Allergy and Infectious Diseases at the National Institutes of Health, urged clinicians faced with a patient with respiratory symptoms and a fever—the signs of coronavirus—to ask them if they have traveled to China.

The emergence of other diseases in the past two decades—including SARS, MERS and Ebola—demonstrates the need for action, the authors said.

Adding travel history as a vital sign would require training for all members of the healthcare team on how to integrate key epidemiologic information into their risk assessments in much the same way clinicians are trained to ask about tobacco use to assess a patient’s risks for cancer and heart disease.

“We believe that the urgent threat of communicable diseases makes the collection of travel history necessary,” the authors wrote.

Both MERS and SARS were associated with specific travel. MERS was associated with travel to the Arabian Peninsula and SARS was associated with travel primarily to Hong Kong, Singapore and Beijing, the authors noted. “Currently COVID is similar in that there are geographic clusters, but those lines may be blurring as the outbreak expands,” Perl said.

Perl and Price said ascertaining travel history is critical to protect both patients and those caring from them. They noted that in 2014, a patient presented to a Dallas emergency department after returning from Liberia with low-grade fever, abdominal pain, dizziness, nausea and headache. The patient had Ebola, but clinicians did not include travel history in the patient’s vitals and the diagnosis was initially missed, compromising the well-being of the patient and caregivers.




Christopher Jason


Researchers at Atrius Health in Massachusetts found that the use of EHR data is an accurate way to predict hospitalizations with a diverse group of patients.

Predictive tools using EHR data, claims data, and combined data all possessed similar predictive value when identifying potential hospitalizations in a six month period, according to a study in the American Journal of Managed Care.

Predicting hospitalization among a diverse group of patients is a difficult process, but can be useful for determining provider workflows and allocating hospital resources. The use of EHR data is an accurate way to predict hospitalization.

“The healthcare system generates, collects, and stores a tremendous amount of data during the course of a patient’s clinical encounter, with one study finding an average of more than 200,000 individual data points available during a single hospital stay,” the authors wrote.

“These data are used to monitor a patient’s progress, coordinate care among all members of the healthcare team, and provide documentation for billing and reporting activities. Although the use of data for these purposes has been long-standing, the availability of these data has increased substantially.”

Researchers analyzed EHR data adult patients seen at Atrius Health, a large multi-specialty group in Massachusetts, from June 2013 to November 2015. To get a broad sample size, they selected patients with different demographics, medications, clinical dosages, and prior utilization. Some were insured under Medicare, Medicaid, and commercial contracts.

“Data sets capable of linking EHR and claims data at the patient level remain uncommon,” wrote the authors. “We hypothesized that when combined, these two data sources would complement each other and lead to stronger prediction than that observed previously.”

From there, researchers developed three different types of models to predict hospitalization within six months, including EHR-only, claims-only, or both EHR-and-claims data.

Overall, 185,388 patients were included for analysis. With this large and broad sample size, a variety of predictive characteristics were observed such as age, prior healthcare utilization, and risk level.

Throughout the study, researchers were able to develop a risk score that accurately projected hospitalization in the following six months. Using the area under the receiver operating curve, a measure used to determine prediction model accuracy, the researchers found that using only EHR data, only claims data, or combined data sources were nearly all equally accurate.

EHR-only and claims-only data both received a 0.84 score, while the combined claims and EHR data received a 0.846 score.

“Although our results suggest some utility to combining EHR and claims data to inform predictive model creation, we find that even in scenarios in which only EHR or claims data are available, strong performance can be achieved provided that a diverse collection of variable types is represented,” explained the authors.

“The risk prediction score was also found to be well calibrated in those less likely to be hospitalized in the next 6 months, but it did become less accurate among those at higher risk of hospitalization,” they continued. “The model tended to overestimate the likelihood of hospitalization in those with higher than 30% predicted risk, likely owing to the small number of patients demonstrating such high risk.”

One major limitation was that all the data was derived from one health system without an external center to authenticate results. Still, researchers do believe that any health system could apply these same methods to adjust the model for its own patients and system of care.

“We believe that our model approach is a meaningful step toward identifying patients at highest risk of hospitalization,” the authors concluded. “Tying the model to care interventions that are likely to modify the risk of hospitalization represents a promising area for future research.”




Aviva Bodek


Antibiotic resistance is one of the greatest threats to global health today. In the U.S. alone, more than 2.8 million antibiotic-resistant infections occur each year, and more than 35,000 people die as a result. This translates to a death every 15 minutes as a result of an antibiotic-resistant infection. Future projections suggest that by 2050, 10 million mortalities a year will result from antibiotic-resistant bacteria, leading to a 2 to 3.5% reduction in Gross Domestic Product and costing the world up to $100 trillion.

Antibiotic resistance occurs naturally, but overuse of antibiotics is drastically accelerating the process. Reducing the profligate use of antibiotics is a crucial element in preventing further bacterial resistance from emerging.
 

Reducing Antibiotic Use in the Hospital

Hospital antibiotic stewardship programs (ASPs) can reduce inappropriate antibiotic use and are now a mandatory entity in all U.S. hospitals. The new rule from the Centers for Medicare & Medicaid Services (CMS) dictates that all hospitals will be required to have infection prevention and control and stewardship programs in place by March 30, 2020 in order to receive payments from the agency. Hospital accrediting bodies such as The Joint Commission have required antibiotic stewardship programs in hospitals since 2017.
 
An ASP is designed to provide guidance for the safe and cost-effective use of antibiotic agents. This evidence-based approach addresses the correct selection of antibiotic agents, dosages, routes of administration and duration of therapy. Such programs not only decrease antibiotic resistance, adverse drug events and hospital length of stay, but they can also save money for healthcare institutions. A University of Maryland study, for example, showed $3 million in cost savings in the first three years of an ASP.

According to the CDC, 20 to 50% of antibiotic use is unnecessary and inappropriate. While preventing infections is the first defense against antibiotic resistance, perhaps the single most important action needed to greatly slow down the development and spread of antibiotic-resistant infections is to change the way antibiotics are prescribed.

Examples of interventions to prevent overuse or overprescribing of antibiotics in hospitals include:

  • Use of rapid diagnostic testing
  • Restricting broad-spectrum antibiotics
  • Shortening the duration of therapy through automatic stop orders
  • Basing treatment on patient pharmacokinetic and pharmacodynamic characteristics
  • Developing institution-specific treatment guidelines
  • Therapeutic review with comprehensive feedback and provider education.


Leveraging Technology to Optimize Antibiotic Therapy

With so many components to a successful ASP, technology, such as clinical decision support systems (CDSSs) is critical to maximize program efficiency and effectiveness. Based on a sophisticated set of customizable rule engines, CDSSs can provide prompts and reminders to assist healthcare providers in implementing evidence-based clinical guidelines at the point of care.

CDSSs operate by continuously monitoring patient data to alert clinicians in real time of potential infections, drug-bug mismatches, multidrug-resistant organisms, isolation candidates, test results and reportable infections. These alerts help to identify patients for potential interventions. Several studies have demonstrated that CDSS implementation increases the number of antibiotic interventions that can be made by as much as 87%.  A significant time reduction in both de-escalation and escalation to appropriate antibiotic therapy has also been demonstrated.

Customization of the electronic health record (EHR) is another electronic tool that can bolster an ASP program. EHR capabilities may include tracking interventions, dose checking alerts, best practice guided order sets, antibiotic time-outs, antibiotic restriction processes, intravenous to oral conversion monitoring and tracking antibiotic prescribing practices.
 

Reporting on Facility and State Improvements

Technology can also be a major asset for the electronic reporting of antibiotic use and antibiotic resistance data to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN). Although currently voluntary, NHSN antibiotic use and/or antibiotic resistance reporting has been identified as one option for eligible hospitals to meet Public Health Registry reporting criteria under Stage 3 of the CMS Meaningful Use (MU3) Program. The information must be submitted electronically in Health Level Clinical Document Architecture format. Manual data entry is not available for the Antibiotic Use and Resistance Module.

Collecting, analyzing and reporting data on antibiotic use and then using that information to improve prescribing practices is the foundation of a successful ASP leading to decreased antibiotic resistance. Technology such as CDSSs and EHR customization not only enables program metric tracking and reporting, it allows an ASP to reach a larger patient population and optimize resource utilization.


Jessica Kent


Through a donation from Millennium Health, HHS will leverage real-time data to develop targeted approaches to fighting the opioid crisis.


The Department of Health and Human Services (HHS) has announced an agreement with Millennium Health, an accredited specialty laboratory, to combat the opioid crisis using near real-time drug testing data.

Millennium Health’s Emerging Threat Intelligence Program will donate data that includes regular reporting of drug use trends from definitive urine drug test results obtained from all 50 states, the District of Columbia, and multiple medical practice types that can identify community level indicators of illicit drug use.

The de-identified and aggregate data will enable enhanced surveillance and analysis of emerging drug use trends, allowing officials to spot patterns and target drug use interventions.

In a report published in JAMA Network Open on January 3, 2020, researchers evaluated Millennium Health data and found a sharp rise in the use of methamphetamine and increases in stimulant-involved overdose deaths. Since 2016, rates of positive urine drug test results have increased by 42.44 percent for methamphetamine and 75.46 percent for fentanyl.

The results also showed that among urine drug test results positive for fentanyl, methamphetamine positivity continued to increase by 153.51 percent.

Researchers highlighted the significant role that near real-time data could play in combating these trends.

“Nontraditional data sources, including urine drug tests, may provide a more timely estimation of emerging drug use prior to the reporting of drug overdose deaths. Earlier identification of these trends supports the development of targeted interventions to curb the effect of drug abuse on public health,” the report authors said.

Other organizations have recognized the benefits of using real-time data to track and monitor illicit drug use. The New Jersey Institute of Technology (NJIT) recently developed a real-time data analytics tool that helps treatment centers and counselors identify and treat drug abuse. The tool monitors online platforms like Twitter and Reddit and combines this information with geospatial data to find out where users are obtaining drugs, as well as trends or changes in drug use.

The Millennium Health data donation will build on HHS’ efforts to curb the effects of the opioid epidemic. In August 2019, the agency partnered with the Healthcare Resources and Services Administration (HRSA) to award nearly $400 million in grants to expand patient access to opioid treatment in rural communities and other medically underserved areas.

“Health centers and behavioral health providers are on the front lines of the fight against the opioid crisis and substance abuse, especially in rural communities,” HHS Secretary Alex Azar said in a statement.

“With our evidence-based strategy, HHS is working to support local communities in fighting back against substance abuse, and our united efforts are yielding results. Together, we can end our country’s opioid crisis and lay a foundation for a healthier country where every American can access the mental healthcare they need.”

The new agreement with Millennium Health will give HHS access to timely data that can support the development of focused healthcare services at state and local levels. HHS expects that the collaboration will result in services that can prevent drug overdoses, reverse non-fatal overdoses, and engage individuals in treatment.

“The Trump Administration recognizes the power of current data in the multi-pronged efforts to curb the drug overdose epidemic. With frequent reporting of drug testing data, HHS can work with city, county and state public health officials to provide resources to help reduce crisis points and save lives,” said Assistant Secretary for Health Admiral Brett P. Giroir, MD. “The donation of this data is critical for reducing the occurrence of the substance use epidemic and reaching the people who need help most.”


Aviva Bodek 


Antibiotic resistance is one of the greatest threats to global health today. In the U.S. alone, more than 2.8 million antibiotic-resistant infections occur each year, and more than 35,000 people die as a result. This translates to a death every 15 minutes as a result of an antibiotic-resistant infection. Future projections suggest that by 2050, 10 million mortalities a year will result from antibiotic-resistant bacteria, leading to a 2 to 3.5% reduction in Gross Domestic Product and costing the world up to $100 trillion.

Antibiotic resistance occurs naturally, but overuse of antibiotics is drastically accelerating the process. Reducing the profligate use of antibiotics is a crucial element in preventing further bacterial resistance from emerging.
 

Reducing Antibiotic Use in the Hospital

Hospital antibiotic stewardship programs (ASPs) can reduce inappropriate antibiotic use and are now a mandatory entity in all U.S. hospitals. The new rule from the Centers for Medicare & Medicaid Services (CMS) dictates that all hospitals will be required to have infection prevention and control and stewardship programs in place by March 30, 2020 in order to receive payments from the agency. Hospital accrediting bodies such as The Joint Commission have required antibiotic stewardship programs in hospitals since 2017.
 
An ASP is designed to provide guidance for the safe and cost-effective use of antibiotic agents. This evidence-based approach addresses the correct selection of antibiotic agents, dosages, routes of administration and duration of therapy. Such programs not only decrease antibiotic resistance, adverse drug events and hospital length of stay, but they can also save money for healthcare institutions. A University of Maryland study, for example, showed $3 million in cost savings in the first three years of an ASP.

According to the CDC, 20 to 50% of antibiotic use is unnecessary and inappropriate. While preventing infections is the first defense against antibiotic resistance, perhaps the single most important action needed to greatly slow down the development and spread of antibiotic-resistant infections is to change the way antibiotics are prescribed.

Examples of interventions to prevent overuse or overprescribing of antibiotics in hospitals include:

  • Use of rapid diagnostic testing
  • Restricting broad-spectrum antibiotics
  • Shortening the duration of therapy through automatic stop orders
  • Basing treatment on patient pharmacokinetic and pharmacodynamic characteristics
  • Developing institution-specific treatment guidelines
  • Therapeutic review with comprehensive feedback and provider education.


Leveraging Technology to Optimize Antibiotic Therapy

With so many components to a successful ASP, technology, such as clinical decision support systems (CDSSs) is critical to maximize program efficiency and effectiveness. Based on a sophisticated set of customizable rule engines, CDSSs can provide prompts and reminders to assist healthcare providers in implementing evidence-based clinical guidelines at the point of care.

CDSSs operate by continuously monitoring patient data to alert clinicians in real time of potential infections, drug-bug mismatches, multidrug-resistant organisms, isolation candidates, test results and reportable infections. These alerts help to identify patients for potential interventions. Several studies have demonstrated that CDSS implementation increases the number of antibiotic interventions that can be made by as much as 87%.  A significant time reduction in both de-escalation and escalation to appropriate antibiotic therapy has also been demonstrated.

Customization of the electronic health record (EHR) is another electronic tool that can bolster an ASP program. EHR capabilities may include tracking interventions, dose checking alerts, best practice guided order sets, antibiotic time-outs, antibiotic restriction processes, intravenous to oral conversion monitoring and tracking antibiotic prescribing practices.
 

Reporting on Facility and State Improvements

Technology can also be a major asset for the electronic reporting of antibiotic use and antibiotic resistance data to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN). Although currently voluntary, NHSN antibiotic use and/or antibiotic resistance reporting has been identified as one option for eligible hospitals to meet Public Health Registry reporting criteria under Stage 3 of the CMS Meaningful Use (MU3) Program. The information must be submitted electronically in Health Level Clinical Document Architecture format. Manual data entry is not available for the Antibiotic Use and Resistance Module.

Collecting, analyzing and reporting data on antibiotic use and then using that information to improve prescribing practices is the foundation of a successful ASP leading to decreased antibiotic resistance. Technology such as CDSSs and EHR customization not only enables program metric tracking and reporting, it allows an ASP to reach a larger patient population and optimize resource utilization.


Christopher Jason


Researchers believe future interventions may need to target patient-centered nudges to complete their cancer screenings.


With doctors’ busy schedules and the ongoing issue of clinician burden, an EHR “nudge” may be effective to prompt medical assistants to set up and order cancer screenings for doctors to sign once they see the patient.

Clinician burden is high, reports confirm, and with other conflicting issues like limited patient engagement and EHR use challenges, key elements of care can slip through the cracks. Preventive screenings, like cancer screenings, may not happen as regularly as they should.

But new data, published in JAMA Network Open suggests an EHR nudge could address many of those problems. According to study authors Mitesh Patel, MD, and Esther Hsiang, MD, pushing EHR nudges out to medical assistance – not physicians themselves – could help address gaps in care that pervade the patient experience and drive cancer screening rates.

The EHR was a natural place to start, the researchers said. More than 90 percent of clinicians and health systems now use EHRs, making them accessible for study participants.

“This was a project that was initiated actually by the primary care practices,” said Patel. “We had done a pilot a few years ago showing that a slightly different version of the nudge could potentially work. So, we worked with them to improve the design and then this was rolled out at three other practices and compared to the control groups here.”

Instead, researchers targeted medical assistants specifically to account for physician burnout challenges and EHR complexity that often bogs down physicians.

“Providers, especially primary care providers in the outpatient primary care setting, are expected to do so many different things in terms of addressing patient problems and remembering health maintenance screening, including cancer screenings and often increasingly shorter and shorter visits,” Hsiang said in an interview with EHRIntelligence.com.

Pushing the nudges out to medical assistants was a key strategy for addressing that burden, she continued.

“Just to try to relieve some of that burden, one way to think about it is to what degree can the use of emerging technologies or increased implementation of technologies help to flag some of those things more automatically to help address the issues of health maintenance themselves,” explained Hsiang.

And, ultimately, this approach had positive results.

The researchers, who hailed from the Perelman School of Medicine at the University of Pennsylvania, found a 22 percent increase in screening orders for breast cancer and a 14 percent increase for those treating colorectal cancer. Overall, 88 percent of the breast cancer patients and 82 percent of colorectal patients included in the study had a cancer screening ordered due to the nudges.

But there is room for improvement – and further research – going forward. For one, there are questions about whether these nudges could be sent to other types of providers.

Patel said the standard is to deploy it on the physicians. However, the novelty of this study was to target the assistants to save the physician’s time.

“So instead of physicians responding to alerts, physicians could have conversations with patients about cancer screening,” explained Patel. “It was less time dealing with alerts and more time talking to patients. But, like I said, the standard approach is to alert doctors.”

There’s also the question of patients actually receiving preventive screenings.

Although the percentage of cancer screenings increased, there were minimal changes in the rates of patients who followed through within one year and completed their screenings.

Both authors believe patient-centered nudges should be next, suggesting a path forward for future research. Although there was a major increase in the percent of doctors that order the tests, there was little change from the patient, Patel noted. Hsiang believes that patient-centered nudges can be implemented into mobile technology via a smartphone or tablet.

But delivering nudges to patients, potentially through a smartphone or tablet, could help address the patient engagement barriers keeping preventive care access low.

“In this study we found that physicians were ordering these tests appropriately, more so after the nudges were implemented, but the patient completion rates did not increase,” concluded Hsiang. “And I think we have several different hypotheses for what's driving that, but better analyzing it, understanding the different factors that are causing the patients not to get the cancer screenings done is particularly important.”





Michael Walter


Machine learning models using radiomics can help radiologists classify renal cell carcinomas (RCCs), according to new findings published in the American Journal of Roentgenology.

“CT is gradually evolving into a useful imaging tool in renal mass differential diagnosis,” wrote Xue-Ying Sun, First Affiliated Hospital with Nanjing Medical University in China, and colleagues. “Several studies have reported that the use of enhancement threshold levels could help to distinguish RCC subtypes and discriminate RCC from benign oncocytoma with 77–84% accuracy. However, the differential diagnosis of renal mass-forming lesions is still difficult, and a variety of imaging findings have been described with different performance results reported.”

To see how machine learning can help improve such classification, the authors explored contrast-enhanced CT (CECT) scans showing 254 RCCs. A team of radiologists manually segmented lesions so that a full radiologic-radiomic analysis could be performed. A machine learning model was then trained to classify renal masses using a 10-fold cross-validation, and the models’ performance was compared to that of four veteran radiologists.

Overall, when differentiating clear cell RCCs (ccRCCs) from papillary RCCs (pRCCs) and chromophobe RCCs (chrRCCs), the four radiologists achieved a sensitivity that ranged from 73.7% to 96.8% and specificity that ranged from 48.4% to 71.9%. The team’s ML model had a sensitivity of 90% and specificity of 89.1% for that same scenario.

When differentiating ccRCCs from fat-poor angioleiomyolipomas and oncocytomas, the radiologists achieved a sensitivity that ranged from 73.7% to 96.8% and specificity that ranged from 52.8% to 88.9%. The ML model had a sensitivity of 86.3% and specificity of 83.3%.

Finally, when differentiating pRCCs and chrRCCs from fat-poor angioleiomyolipomas and oncocytomas, the radiologists achieved a sensitivity that ranged from 28.1% to 60.9% and a specificity that ranged from 75% to 88.9%. The ML model had a sensitivity of 73.4% and specificity of 91.7%.

“Our results show that routine expert-level radiologist interpretation of CT images had obviously large variances with relatively low accuracy in differentiation of benign and malignant solid renal masses, whereas the radiologic-radiomic ML approach provides an assessment of their ability to aid standardization of CECT interpretation,” the authors concluded. “Our radiologic-radiomic ML model, comprising quantitative radiomic features and a priori radiologic hallmarks that are different from a DL black-box algorithm, was able to significantly reduce the misclassification of renal mass lesions. Considering the interpretability of our radiologic-radiomic ML model, we believe that the radiologic-radiomic ML approach could be a potential adjunct to expert-level radiologist interpretation of CT images for improving interreader concordance and diagnostic performance in routine clinical assessment of renal masses.


A study found that an EHR “nudge” increased breast cancer screenings by 22 percent and colorectal screenings by 14 percent.

Christopher Jason


With doctors’ busy schedules, a “nudge” is needed to prompt medical assistants to set up and order cancer screenings for doctors to sign once they see the patient, according to a study published in JAMA Network Open.

Researchers at Penn Medicine that specialize in EHR nudges found a 22 percent increase in screening orders for breast cancer and a 14 percent increase for those treating colorectal cancer. Overall, 88 percent of the breast cancer patients and 82 percent of colorectal patients included in the study had a cancer screening ordered due to the nudges.

Although the percentage of cancer screenings increased, there were minimal changes in the rates of patients who followed through within one year and completed their screenings. Researchers concluded that further interventions may need to be targeted to patients to complete their screenings.

“Cancer screening involves both the clinician recommending and ordering it as well as the patient taking action to schedule and complete it. Our study found nudges can be very influential, but for cancer screening they likely need to be directed to both clinicians and patients,” said Mitesh Patel, MD, MBA, director of the Penn Medicine Nudge Unit and the senior author of the study.

The study of nearly 70,000 breast or colorectal cancer patients at 25 primary care practices looked at how doctors can use the EHR to increase the rate at which they screen patients for the disease.

 “Clinicians are increasingly being asked to do more with a fixed amount of time with a patient,” said Esther Hsiang, the study’s lead author. “By directing the intervention to medical assistants, this reduced the burden on busy clinicians to respond to alerts and instead gave them more time to have a discussion with their patients about screening.”

In the study, the nudge was directed only to medical assistants who could create orders for clinicians to review. The medical assistants would then inform the patients that they were eligible for cancer screening and should discuss screening with their clinician. Researchers targeted medical assistants specifically to account for physician burnout challenges and EHR complexity that often bogs down physicians.

This study design lessened the burden for physicians and encouraged patients to prioritize a discussing a cancer screening. However, due to the more arduous process for completing a screening order – patients usually have to schedule a second appointment – patients did not necessarily respond to the prompts.

“Once cancer screening is ordered, the patient still has to take several steps to complete it,” explained Patel. “That includes scheduling an appointment, sometimes conducting prep — such as bowel prep for a colonoscopy — and then going to the appointment. These several steps can add up to high hurdles, especially if patients have lower motivation to begin with. Future interventions should test ways to nudge patients to complete cancer screenings.”

Patel and his team are in the process of developing a new study to test nudges for both the clinicians and the patients to increase the likelihood of patients to follow through and complete their screenings. The researchers also want to branch out and gain more data from more than the two types of cancer that they initially focused on.

“Since EHRs are used by more than 90 percent of physicians, this is a really scalable approach,” Patel concluded. “It is likely that it could be successful for other types of screening.”


Care coordination and interoperability between health data strengthens levels of care and reduces healthcare costs.


Christopher Jason


Driving care coordination is essential to providing a quality patient experience, helping to tie together patient care at the many healthcare facilities she may visit. But limited health data and EHR interoperability can get in the way, limiting providers’ ability to access patient information from disparate facilities.

Interoperability enables care coordination to deliver a patient’s health data from multiple providers and specialists. With patients attending different hospitals and specialists, the need for interoperability between multiple providers is key. Coordinated care reduces healthcare costs by eliminating repetitive tests and procedures.

Strong EHR use is found at the primary care level. However, it is still a work in progress at acute and post-acute hospitals.

Care coordination crucial to cohesive primary care

Strong EHR use is key for better care coordination between primary care and behavioral health specialists, said researchers in a 2017 study published in the Journal of the American Board of Family Medicine.


The study found that 67 percent of individuals with behavioral health (BH) disorders do not receive the care that they need, but when their care is integrated into the primary care setting, that issue typically improves.

“Most patients with BH conditions, including children, are seen in medical settings, most commonly primary care (PC), presenting the need and opportunity to replace separated systems of care that do not adequately meet the needs of patients with integrated, ‘whole-person’ care,” the researchers explained.

Integrating and coordinating specialty care — in this case behavioral healthcare — into primary care relies on EHR use and interoperability. Interoperable systems allow providers to access valuable clinical information from other providers who have previously treated the patient.

“Establish standard processes and infrastructure necessary for your integrated care approach: workflows, protocols for scheduling and staffing, documentation procedures, and an integrated EHR,” the researchers recommended.

And ultimately, this will streamline patient care. Interoperable systems between specialty and primary care providers ensures the specialty provider understands the patient’s current health conditions and can make informed medical decisions.


For example, when specialty providers can access the patient’s complete medical history, they can avoid re-testing and ensure that the patient receives the best care right away.

“This allows the caregiver to quickly find information about that patient and who’s responsible for them,” Mobile Heartbeat Vice President Jamie Brasseal told EHRIntelligence.com. “Providers can communicate with the appropriate colleagues — such as specialists or pharmacists or case mangers — very quickly, and without having to leave the patient’s bedside, or go search for that information at the nursing unit or in the EHR.”

Care coordination improving at acute care hospitals

Patients do not always receive acute or emergency healthcare in the same facility where they receive their primary care, which can create some data exchange challenges for acute care providers. With patient data stored in disparate systems, acute care providers can be left without critical information off of which they can base medical decisions.

In a recent survey from PointClickCare, 49 percent of acute care providers said they have very little ability to access or share patient data electronically, resulting in a struggle for providers.


“With better communication between the facilities, we would cut back on readmission and sending patients back to the ER and any sort of miscommunication,” said one participating hospital executive.

Reassuringly, many acute care hospitals are investing and focusing more on improving its data exchange efforts.

Seventy-three percent of acute care providers said they are putting a higher priority on implementing interoperable systems for transferring patients.

“Streamlining interoperability between systems creates huge opportunities for cost reduction, patient care improvement and reduced workloads for people on both ends of patient transfers,” researchers said.

“This type of health data exchange also helps improve the transparency of data between acute care and skilled nursing facilities, enabling a stronger relationship. And, it enables robust, population health capabilities that are scalable as the number of patients needing post-acute care grows.”

In a 2018 report from the ONC, 83 percent of hospitals that had the capabilities to send, receive, locate, and integrate patient health information from outside organizations into their EHR systems reported having the ability to access information electronically at the point of care.

“This is at least 20 percent higher than hospitals that engage in three domains and almost seven times higher than hospitals that don’t engage in any domain,” wrote Don Rucker, MD, national coordinator for health IT and Talisha Searcy, director of research and evaluation.

Educating the staff and providers on EHR use and information exchange will benefit the team in the long run and provide better care for patients in acute care

Promoting better EHR adoption in post-acute care

Interoperability challenges can follow patients and providers out of the hospital and into the rehabilitation process. Provider access to information about a patient’s acute hospital stay will be integral to quality post-acute care, but many providers see bumps along the road.

That same PointClickCare survey revealed that 84 percent of post-acute care organizations are still using at least some manual processes to exchange patient data with acute care hospitals. Organizations relying on fax, email, and paper-based solutions to exchange patient data could encounter mistakes, mismatched patient data, or omissions that could seriously hinder patient care.

But the Centers for Medicare & Medicaid Services (CMS) is working to address that gap.

After prompting nearly universal EHR adoption in acute care facilities, CMS is promoting widespread EHR adoption in post-acute care (PAC) settings.

In March 2019, the federal agency released a request for information (RFI) seeking input about the best ways to incentivize EHR adoption and use among providers in the post-acute setting

“PAC facilities are critical in the care of patients’ post-hospital discharge and can be a determining step in the health progress for those patients,” stated CMS in the RFI.

“Interoperable health IT can improve the ability of these facilities to coordinate and provide care; however, long-term care and PAC providers, such as nursing homes, home health agencies, long-term care providers, and others, were not eligible for the EHR Incentive Programs under the HITECH Act,” the federal agency explained.

CMS partly attributes the slow rate of EHR adoption in PAC settings to the lack of federal incentives available to PAC providers.

Nearly 65 percent of skilled nursing facilities used an EHR system in 2016, but rates of health data exchange remained low among this population of providers. Only 30 percent of skilled nursing facilities participated in health data exchange, and only seven percent had the ability to locate and integrate patient health data into patient EHRs.

The inconsistency between rates of EHR adoption in acute and ambulatory care settings and PAC facilities partly contributes to problems with transitions of care.

“For PAC facilities that do possess EHRs, vendor adoption of interoperable functionality has been slow and uneven,” stated CMS.

As the medical industry continues to become increasingly digital and complex, it will be essential for disparate organizations to have systems for exchanging data. Interoperable tools will help drive care coordination between primary care providers, specialists, and acute and post-acute care organizations. And in doing so, clinicians can work to drive whole-person health and efficient, quality care.


Stephen Lawless


When you bring your loved one to the hospital, you expect them to get better, not worse.

But too often, we are failing at this crucial task. Too often, we hear about a patient admitted to the hospital who is seemingly doing fine, and then suddenly goes downhill. The question is “How did they get worse right under our eyes?”

How do we prevent someone from getting much sicker without us even realizing it?

Sepsis kills almost 5,000 children annually in the U.S.—more than cancer—and costs about $7.3 billion for hospitalizations alone. This huge and growing burden is now the most expensive cause of hospitalization in the U.S., with a high fatality rate that makes early recognition of patient instability absolutely critical. 

Innovation has finally caught up to this age-old issue by harnessing the power of predictive analytics. Three years ago, at Nemours Children’s Health System, a multidisciplinary system-wide team built a sepsis response tool that capitalizes on the health system’s technological resources.

Proprietary scoring criteria is built into the electronic health records to predict patient downturns before they happen. These stats are monitored by paramedics running the health system’s Clinical Logistics Center, a virtual command post that monitors every child seeking inpatient care at our free-standing children’s hospitals in Florida and Delaware.

Like air-traffic controllers peering into multiple video monitors, our team of paramedics and emergency nurses closely tracks color-coded vital signs in green, yellow or red to detect subtle changes in biomarkers that predict whether a patient is stable, declining or needs immediate attention. They triage alarms and can instantaneously initiate a rapid response team or even tap into a high-resolution audio/video connection, available in every room, to provide instant virtual care.

Machine learning and sophisticated algorithms that enable us to practice predictive analytics are not just aimed at speeding up our response to alarms. Our Clinical Logistics Center creates a smart support system that eases the alarm fatigue of nursing staff, acts as a fail-safe for patient care and can be a valuable planning tool to anticipate critical staffing needs in advance.

Nowadays, America’s hospitals have sicker patients on the general floors, patients who 20 years ago would have been in the ICU. Many of them are existing in what one expert calls “a precarious state of pseudo-stability,” and most hospitals are unprepared when they unexpectedly deteriorate and need instant, life-saving therapies. Without rapid intervention, patients who go into septic shock have an overall mortality rate of more than 50 percent.

Since we set up our response system at Nemours, we have had no unexpected deaths due to sepsis, largely because no alarms go unanswered for more than 90 seconds and no patients can suffer a severe downturn without staff being quickly alerted. Overall, we have reduced medication errors through decision supports, improved patient and nurse satisfaction rates, and, most importantly, we have dramatically lowered the frequency of sepsis from 2 percent to .05 percent.

Recently, we were honored to be the only pediatric health system invited by the Centers for Medicare and Medicaid Services to participate in a national sepsis “listening session” among subject-matter experts and leaders in the fields of innovation, care delivery reform and implementation science. CMS’s initiative is a most welcome development in promoting early identification of high-risk sepsis patients, speeding care delivery, and enhancing nutrition, mobility and other measures to improve quality of care.

Stakeholders in the fight against sepsis are encouraged by the emerging possibilities for using “big data” and artificial intelligence. CMS heard pleas for more funding towards awards and prizes that would foster these and other innovations. Participants called for raising community awareness and improving coordination between first responders and emergency departments.  

With the backing of federal, state and local health officials, and the willingness to promote coalition-building, we can replicate and expand upon the efforts that we and other healthcare systems have launched. We can create a better system that can be a model for fighting serious diseases and for saving lives.




Samara Rosenfeld


new imaging tool created by the National Institutes of Health (NIH)-led Brain Research through Advancing Innovative Neurotechnologies (BRAIN) makes it possible to capture images of more protein targets faster than traditional methods.

The imaging tool allows researchers to view dozens of proteins in a single tissue sample with thousands of neural connections.

The tool produces a rainbow of images, each one capturing different proteins with the complex network of synapses. The proteins could be present in different amounts and locations in a network.

“Such findings may shed light on key differences among synapses, as well as provide new clues into the roles that synaptic proteins may play in schizophrenia and various other neurological disorders,” wrote Francis Collins, M.D., Ph.D., director of NIH.

Researchers at Massachusetts Institute of Technology and Harvard University adapted an existing imaging method called DNA PAINT to better observe working synaptic proteins — something that has often presented many obstacles for researchers.

The adapted method is called PRISM (Probe-based Imaging for Sequential Multiplexing).

Researchers labeled proteins and molecules using antibodies that recognize the proteins. The antibodies include a DNA probe to help make the proteins visible through a microscope.

In DNA PAINT, strands of DNA bind and unbind to create a blinking fluorescence captured using super-resolution microscopy. This method, researchers said, is very slow.

To overcome this, the research team altered the DNA probes using synthetic DNA designed to bind more tightly to the antibody.

PRISM helped researchers go through the imaging process more quickly, though the resolution is slightly lower. While the research team currently captures 12 proteins in a sample in about an hour, it is possible the number could increase to 30, the researchers reported in the journal Nature Communications.

“PRISM will help (researchers) learn more mechanistically about the inner workings of synapses and how they contribute to a range of neurological conditions,” Collins wrote.


Mark Byers


The Department of Veterans Affairs (VA) is going through a time of great transformation. Much of this change is being driven by new models of healthcare delivery, the transition to value-based care, new mandates for federal agencies to modernize legacy systems, emerging innovations, as well as the new electronic health record modernization (EHRM) program.

Many of these changes point to the enhanced need to standardize proven technologies and processes to reduce the variance of care across the Veterans Health Administration (VHA), bring about best practices to each VA medical center and ensure that veteran care does not lag during the later facility deployments of the EHRM program.

By putting into place a system to identify and evaluate best of breed IT solutions across the continuum of VA medical care, the VA will be able to further improve patient care at all VA medical facilities and improve the employee experience by streamlining workflow and increasing productivity and efficiency. The VA will also receive the benefit of improved contract provisions and data standardization.

One of the most significant challenges is the transition of specialty health care applications within the current VistA ecosystem in both the short- and long-term as progress is made regarding the migration to the new, commercial-off-the-shelf EHRM. This is an important step in the process over the next 10 years and beyond.

In addition, as medical technologies continue to evolve at an exponential pace, we must ensure that veterans have access to the same quality of care that is delivered in the private sector – the pace of improvements in medical care will not stand still during the deployment of the new VA EHR.

Why the VistA transition is vital

Since its deployment at the VA in 1994, VistA has evolved into a technically complex system comprised of approximately 200 modules that support health care delivery at more than 1,500 sites of care, including each Veterans Affairs Medical Center (VAMC), Community Based Outpatient Clinics (CBOC) and Community Living Centers (CLC), as well as at nearly 300 VA Veteran Centers.

That makes it essential for the VA to establish a process to review these diverse and valuable applications that provide the interface of patient care, and upon which VA medical professionals rely on to offer safe and reliable healthcare for our nation’s veterans.

As the VA transitions to the new EHR, there is also a need for a similar systematic evaluation and transition of the many modules and applications currently integrated within the VistA environment – including the evaluation of critical business intelligence regarding unique VA requirements. This will help with the overall migration to the new EHR and determine which applications will be developed by the EHR contractor.

Many of these VA medical applications are also already compatible with the Cerner EHR platforms in the commercial health care sector and have proven quality measures outcomes and a cost benefit analysis to healthcare systems.

Evaluating and standardizing existing, best of breed, health care applications within the VistA system can lead to a smoother transition to the new EHR for VA health care providers, reduce cost as these systems are already in place and functioning, maximize dollars already spent and mitigate unnecessary risk in an already highly complex transition.

Standardization reduces risk, ensures quality care

As the VA begins to execute its systematic review of healthcare applications that are in use today, this will shine a light on turn-key modules that are tailored for VA workflows, as well as enhance an all-inclusive integration, continuous enhancements, maintenance and customer support. These improve the safety and quality of services and medical care that is delivered to our nation’s veterans now and into the future.

In addition, the VA would benefit from an analysis of alternatives through the best of breed applications available. Thankfully, the current veteran-focused integration process with all solutions being technical reference model (TRM) and enterprise technical architecture (ETA) compliant is helping.

This can be also achieved through applications that offer flexible and extensible systems of engagement; are standardized to enhance efficiencies; maintain clinician productivity; ensure ongoing veterans access to care; and lower total cost of ownership for the VA.

For example, there are commercially proven software applications that enable electronic clinical surveillance for infectious disease prevention and clinical pharmacy, including opiates. This software solution called TheraDoc provides the ability for immediate medical intervention, which currently is challenging due to the VA’s huge and dispersed population of patients – both inpatient and outpatient.

The Miami VA healthcare facility is also using the LiveData PeriOp Manager to help synchronize surgical scheduling, increasing access to care to 1.8 additional cases per day. This solution improves the patient’s journey from surgical consultation through preoperative steps to the scheduled day-of-surgery and discharge.

Both of the commercial systems highlighted above have been integrated into the VA’s current system, VistA, through the expertise of DSS, Inc.

Conclusion

By standardizing modern and proven solutions across all VA medical centers, it is possible to increase veteran access to care, enhance the veteran experience, and improve the VA employee experience. This also reduces risk during deployment of modernization effort – allowing the VA to effectively manage a time of great transition.



Sara Heath


Nearly one-quarter of patients would opt into data sharing for all of their information with any interested precision medicine research party.

Patients approve of data sharing and are willing to contribute their medical information to research projects, but according to a group of researchers from the University of California San Diego, there may be some strings attached.

These findings come in the context of the precision medicine and All of Us campaigns, which call for the use of patient data repositories to create targeted treatment approaches to improve care quality. Precision medicine efforts rely on patients being will to share data with medical researchers.

“The finding in this study that most patients were willing to share data from their EHRs and biospecimens with researchers is reassuring,” the researchers wrote. “Not only can biomedical research benefit from these resources but also a multisite learning health care system can continuously advance as a result of data-driven improvements to processes and associated outcomes.”

Overall, patients are willing to participate in precision medicine, but there are some caveats, the UCSD researchers reported in JAMA Open Network. A survey of over 1,200 patients revealed that most are willing to share at least some of their medical information with some interested research groups.

Patients filled out one of four surveys: a simple opt-in survey, a simple opt-out survey, a detailed opt-in survey, and a detailed opt-out survey.


The difference between simple and detailed surveys was the amount of data categories for which the patients outlined their data sharing preferences. Patients completing a simple survey had to opt into or out of sharing in 18 data categories, compared to 59 categories in a detailed survey.

Each survey also asked patients about with which types of researchers they would be willing to share individual survey items, including other researchers within their home organization, researchers at another non-profit organization, and researchers at for-profit organizations.

Overall, 67 percent of all survey respondents said they’d be willing to share all of their data with their own healthcare organizations, and 23.4 percent said they’d share all of their information with any interested research party.

This is good news for the precision medicine movement, which relies on a breadth of patient information for actionable insights, said study senior author Lucila Ohno-Machado, MD, PhD.

“These results are important because data from a single institution is often insufficient to achieve statistical significance in research findings,” Machado, who is also professor of medicine, associate dean for informatics and technology in the UC San Diego School of Medicine and chair of the Department of Biomedical Informatics at UC San Diego Health, said in a statement.


“When sample sizes are small, it is unclear whether the research findings generalize to a larger population. Additionally, in alignment with the concept of personalized medicine, it is important to see whether it is possible to personalize privacy settings for sharing clinical data.”

Seventy-three percent of respondents said they were willing to share their medical information, but selectively. They were willing to share at least one piece of data with at least one type of research group.

Most patients were most willing to share their data with researchers from their home institution, followed by separate non-profit institutions, and then finally with teams at for-profit organizations.

“The reluctance to share data and biospecimens with researchers from for-profit institutions needs further investigation because the category aggregates highly different industries and further refinement might reveal subgroups that have higher association with declining to share than others,” the research team said.

“Strategies to convey how data and biospecimens are being used or will be used for research that includes the development of commercial products to improve health outcomes need to be developed and implemented so that patients can provide consent that is truly informed.”


Additionally, the surveys showed that patients were willing to share some, but not all, of their personal information, which could have implications for research teams accessing patient EHRs.

“This finding is important,” wrote the authors, “because the item to withhold may not be of relevance to a certain study, but the current all-or-nothing option, if chosen, would remove that patient’s data from all research studies.”

The researchers pointed out that there needs to be a more sophisticated mechanism by which researchers can access patient EHRs. A medical record should not be prohibited from a study because the patient has withheld one singular piece of personal data, the team said, especially if that data point is not relevant to a specific study.

IT developers should look for ways to stratify patient data sharing to allow for researcher access to more patient records.

Furthermore, the surveys showed that how a provider asks for patient data access is important. Opt-out forms, which assume patient data access unless a patient says they do not want to participate, are more effective than opt-in.

Additionally, whether the patient used the simple or detailed questionnaire had little impact on whether the patient gave permission to share certain types of medical information.

“This is important because a simple form could be used in the future to elicit choices from all patients, saving their time without significantly affecting their privacy preferences,” said Ohno-Machado. “However, different rates of sharing are expected for opt-in and opt-out of sharing clinical records for research.”

These findings are not a panacea for eliciting patient data sharing, Ohno-Machado said. Instead, they point out contradictions that research teams will need to assess when designing data sharing and opt-in communication protocol.

“Institutions currently make decisions on sharing on behalf of all patients who do not explicitly decline sharing. It is possible that asking patients directly would increase the amount of data shared for research,” Ohno-Machado concluded. “On the other hand, it is also possible that some types of research would suffer from small sample sizes if patients consistently decline certain categories of items.”