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Jennifer Bresnick


Cutting costs doesn't mean sacrificing on quality of care if hospitals focus on reducing unnecessary care variations and trimming down on wasteful testing.


High quality and lower costs can indeed go hand-in-hand for hospitals, according to new data from Advisory Board, if healthcare organizations can successfully reduce unnecessary variations in care.

An analysis of more than 460 hospitals revealed that the highest quality facilities delivered lower-cost care for 82 percent of diagnoses included in the study, indicating that investments in patient safety, standardized care delivery methods, and enhanced health IT tools may be worth the effort.

“Care variation reduction (CVR) is one of the few avenues for generating the level of savings needed to withstand downward pressures on hospital revenues without negatively impacting care, and hopefully improving it,” said Steven Berkow, Executive Director, Research at Advisory Board, an Optum, Inc. business.

Hospitals that follow the lead of their highest-quality, lowest-cost peers could save up to $29 million each year, the report added.

Advisory Board researchers derived the potential savings goal from analyzing cost and quality data from more than 20 million patients across 468 hospitals.  They found that the average hospital spends up to 30 percent more to deliver the same care than a hospital in the highest-performing group.

“Our high-performer benchmark is based on high-quality care, not low cost,” explained Veena Lanka, MD, Senior Director, Research at Advisory Board. 

The team explored variations in common quality metrics, such as rates of complications, to assess hospital performance.

“Closing just a quarter of the cost gap for less than 10 percent of the conditions we analyzed could net over $4 million in annual savings for a typical hospital and over $40 million for 10-hospital system—without compromising quality,” Lanka stressed.

However, Berkow pointed out, “Achieving a realistic chunk of this savings opportunity…will require most health systems to rethink how they prioritize, set and embed care standards.”

Reducing variations in care requires a collaborative effort that involves standardizing provider training, carefully choosing the appropriate settings for care, and fostering a greater reliance on meaningful health IT tools.

Reducing emergency room use by redirecting non-emergency cases to urgent care facilities can help to conserve resources in more expensive settings – as long as the urgent care clinics adhere to best practices for antibiotic stewardship and maintain high quality in other areas of care.

To ensure less variation in how services are applied, organizations may wish to consider clinical decision support (CDS) technologies that can ensure that providers are aware of the latest clinical guidelines for treating specific conditions. 

CDS tools may help to reduce unnecessary testing or imaging, and can help providers react more quickly to high-risk conditions such as sepsis.

Trimming down on repetitious or low-value imaging and lab testing can help to prevent billions in wasteful spending that lead to high costs without producing better outcomes. 

In a 2017 study from Health Affairsresearchers found that low-value testing and imaging contributed to more than half a billion dollars in spending per month in Virginia alone. 

Nationally, wasteful spending accounts for nearly a third of all healthcare dollars each year.

At Methodist Le Bonheur Healthcare, tacking the problem of variation in care and high spending involved significant investment in data analytics and health IT tools, explained Arthur Townsend IV, MD, MBA, Chief Clinical Transformation Officer for Methodist Le Bonheur Healthcare.

“Embarking on a journey to reduce care variation can be challenging, but our success is due to dedicated teams of physicians, nurses and administrators, all working toward the common goal of improving every life touched at Methodist Le Bonheur Healthcare,” he said.

The Tennessee-based health system initially targeted unnecessary laboratory utilization and blood transfusions, using data analytics tools to identify opportunities for improvement that would not negatively affect patient care.

The health system then moved on to develop standards of care for stroke and sepsis, creating Clinical Consensus Groups packed with subject matter experts to define guidelines for treating patients with these conditions. 

The experts, including administrative and clinical champions, took a close look at how to improve clinical documentation and standardize care delivery and infuse new best practices into the daily routines of care providers.

As a result of both efforts, the health system saw more than $800,000 in cost savings and revenue enhancements in a single quarter.  Atrial fibrillation is next on the list, promising even more gains in quality and cost.

“We see care variation initiative as the next frontier in improving overall quality and significant cost reduction across the system through physician leadership,” said Michael Ugwueke, president for Methodist Le Bonheur Healthcare.

While Advisory Board’s Lanka noted that it is not likely that hospitals will be able to stamp out all care variation due to differences in patient demographics, clinical severity, and other underlying socioeconomic issues, most hospitals will have some opportunities to reexamine care delivery and the costs associated with unnecessary utilization or discrepancies in delivery.

The goal is a very high priority for hospitals and health system, according to an accompanying survey of C-suite executives, with “preparing the enterprise for sustainable cost control” taking the top spot on their checklists for the remainder of 2018.

Organizations that hope to achieve that objective will benefit from assessing their current clinical processes for high-cost conditions, considering new technologies to support adherence to clinical guidelines, and investing in innovative initiatives to engage providers in quality improvements that simultaneously lower costs.



Brita Hansen


The medical world has declared zero tolerance for healthcare-associated infections (HAIs), but it is a massive problem to address. 

Seven out of every 100 hospitalized patients at any time and about 30% of patients in intensive care units will acquire at least one HAI, according to the World Health Organization.

HAIs such as Clostridium difficile (C. diff) and catheter-associated urinary tract infections (CAUTIs) take a heavy toll on patient outcomes and length of hospital stay. They are also expensive. The most common infection is CAUTI, accounting for more than 30% of HAIs, costing health systems about $500 million annually in the direct cost of treating patients. 

Making matters worse, Medicare does not reimburse for certain HAIs, and a portion of reimbursements are withheld for the quartile of hospitals with the most HAIs. When Medicare penalties and lost revenues are included, the cost likely exceeds $1 billion annually. 

Thankfully, health systems already have a powerful weapon that can make a major dent in infection rates: electronic health records. For true progress to be made toward zero HAIs, healthcare needs a greater focus on using this tool along with key clinical processes to guide the delivery of care.

Here are five specific areas where health systems can and must improve:

  1. Streamline workflows. Hospitals are reducing infection rates by using EHR systems to streamline workflows by making the right thing to do the easiest thing for clinicians to do. The idea is to remove unnecessary variation in the way care is delivered to ensure clinicians follow the best clinical practices. For example, it was once routine to place catheters in many hospitalized patients, but now catheters should only be used when the patients meet specific clinical criteria. Once inserted, EHR systems can be leveraged to remind clinical staff that a patient has a catheter and suggest they remove it or document why it is still indicated. Up to 69% of CAUTI cases can be prevented by following these and other evidence-based strategies.
     
  2. Stop overtesting. Hospital staff too often overtest for infections when there are not strong clinical reasons to expect an infection. For example, hospitals overtest for C. diff, the most common infectious cause of healthcare-associated diarrhea and a significant factor leading to morbidity, mortality and increased length of stay among hospitalized patients. This inappropriate C. diff testing leads to false positives and overdiagnosis. Using EHR systems to ensure care processes are aligned with current testing guidelines and workflow best practices can produce even bigger improvements. One hospital following this approach had a 50% reduction in C. diff infections.
     
  3. Focus on best practices. Getting CAUTI rates to zero means clinicians should set strict rules for testing and focus on the correct insertion, maintenance and removal of catheters. A study published in Infection Control & Hospital Epidemiology found that when ICUs at the Cleveland Clinic improved their catheter protocols that CAUTIs decreased from three per 1,000 catheter days to 1.9. Similar improvements can be made by ensuring clinicians focus on best practices throughout the care-delivery continuum.
     
  4. Minimize patient risk of infection. Whether it’s overuse of catheters or antibiotics, more rigorous hospital workflows can minimize infection risk. That was among the topics discussed in June at the Association for Professionals in Infection Control and Epidemiology (APIC) conference, where more than 4,500 infection prevention professionals gathered to discuss strategies to lower infection rates. The conference revealed that many hospitals and health systems still have multidisciplinary teams performing retroactive chart reviews of HAI cases to look for things that could have been done differently to mitigate the risk of infection. However, such retroactive assessments can never find the type of consistent process improvements that can be identified by a well-organized EHR, where you can look at and sort large data sets to find patterns. Rather than reviewing past cases, EHRs can even be set up to proactively make recommendations for clinicians. 
     
  5. Improve infection controls. Once an infection is identified in a timely fashion, hospitals need consistent procedures to stop it from spreading. Those should include isolating the patient, clinicians wearing special gowns and gloves, special hand-washing procedures and, crucially, communicating those standards to staff.

It’s not uncommon in American hospitals for nurses to attempt to manage catheter hours by physically walking around wards, seeking out patients with catheters to assess whether proper care was performed and which catheters can be removed. 

In an age when we use technology for everything from better navigation to movie recommendations, hospitals should use modern information technology available to them to push their HAI rates to zero as quickly as possible.



Jessica Kent 


The data sharing policy will support big data analytics by allowing investigators to reuse information and speed up innovations in health research.

The Patient-Centered Outcomes Research Institute (PCORI) has approved a new policy that will encourage data sharing among researchers with the goal of accelerating big data analytics and secure health information exchange.

The new policy strengthens PCORI’s commitment to open science by allowing researchers to verify and build on past findings from PCORI-funded studies and generate new evidence for healthcare decision-makers.

Research teams that have received PCORI funding will place the data generated from their studies, as well as documentation for how that data was produced, into a repository designated by PCORI.

The data, which could include deidentified participant information, full protocols, metadata, and statistical analysis plans, can then be made available for other research teams for additional analysis. PCORI will also provide funding to researchers so that they can prepare the data and other materials for sharing.

“Through this data sharing policy, we’re taking a major step in advancing open science,” said PCORI Executive Director Joe Selby, MD, MPH.

“By supporting how others may use information generated by the studies we’ve funded, we’re helping to enhance the quality and increase the quantity of evidence for healthcare decision making. We’re also reducing redundancy in collecting clinical data sets, which can speed research and the production of more useful evidence.”

PCORI will also require that all personal health information is de-identified to protect the privacy of study participants.

Additionally, informed consent from study participants is required to permit the reuse of data.   PCORI will review requests before granting access.

The new data sharing policy is part of a series of initiatives from PCORI that aim to support research transparency and ensure broad availability of high-quality health data assets.

The organization’s policy on peer review and public release of research findings ensures that all results from PCORI-funded studies undergo a review and are made publicly available on PCORI’s website in a final research report.

In addition to the reports, PCORI offers brief summaries of studies and their findings that are posted as public and professional abstracts on the website.

The Institute also has a public access policy in place to cover the costs for journals to make papers presenting the results of PCORI studies freely available to the public.

By approving this new data sharing policy, PCORI expects to expand on these past initiatives and accelerate healthcare innovation.



Jennifer Bresnick 


The combination of artificial intelligence and the Internet of Things will support the development of smart hospitals and fuel the ongoing growth of big data analytics.

The Internet of Things (IoT) is expected to combine with the power of artificial intelligence, blockchain, and other emerging technologies to create the “smart hospitals” of the future, according to a new report by Frost & Sullivan.

The IoT – also commonly known in the healthcare industry as the Internet of Medical Things (IoMT) – consists of any and all medical devices, patient monitoring tools, wearables, and other sensors that can send signals to other devices via the internet.

These tools generate massive amounts of data that must be stored, integrated, and analyzed in order to generate actionable insights for chronic disease management and acute patient care needs.

IoT data is a valuable addition to other clinical data sources, such as the electronic health record (EHR), that allow providers to monitor patients on an ongoing basis or predict changes in an individual’s health status.

“Escalating demand for remote patient monitoring, along with the introduction of advanced smartphones, mobile applications, fitness devices, and advanced hospital infrastructure, are setting the stage for establishing smart hospitals all over the world,” says the report.

Predictive analytics strategies are beginning to rely on the availability of data from wearables and IoT devices both inside and outside of the hospital. 

Predicting patient deterioration or infection in the inpatient setting requires continuous feedback from bedside devices, while home monitoring tools such as Bluetooth-enabled blood pressure cuff, scales, and pill bottles can keep patients adherent to chronic disease management protocols outside of the clinic.

According to a recent analysis by Deloitte, more than two-thirds of medical devices will be connected to the internet by 2023, compared to just 48 percent of devices in 2018. 

The uptick in connected devices will lead to the availability of more data for analytics, which will in turn require novel methods of extracting meaning from raw datasets.

Artificial intelligence and machine learning strategies are ideally adapted to managing and analyzing continuous data streams in large amounts, says Frost & Sullivan, and will be critical for ensuring that actionable insights are presented to providers without overloading their workflows.

“Sensors, artificial intelligence, big data analytics, and blockchain are vital technologies for IoMT as they provide multiple benefits to patients and facilities alike,” said Varun Babu, Senior Research Analyst, TechVision.

“For instance, they help with the delivery of targeted and personalized medicine while simultaneously ensuring seamless communication and high productivity within smart hospitals.”

The potential to improve efficiency, engage patients continuously, and get ahead of adverse events has created a significant commercial opportunity for device manufacturers, software vendors, and analytics developers, adds a separate report by MarketersMedia.

Currently, the global IoT market is valued at $20.59 billion, and is anticipated to grow at a 25.2 percent compound annual growth rate (CAGR) until 2023 to reach $63.43 billion.

The market includes implantable tools, such as cardiac devices, as well as internet-connected ventilators, imaging systems, vital signs monitors, respiratory devices, infusion pumps, and anesthesia machines, MarketersMedia says.

Frost & Sullivan also anticipates that emerging categories of IoT devices, including adhesive skin sensors, will contribute to the financial opportunity, while developing technologies, such as blockchain, will enhance the security, interoperability, and analytics potential of these tools.

In order to succeed, providers and developers will need to collaborate on creating and deploying data standards and shared protocols to ensure the seamless exchange of data across disparate systems.

“The main objective of IoMT is to eliminate unnecessary information within the medical system so that doctors can focus on diagnoses and treatment,” said Babu. 

“Since it is an emerging technology, technology developers need to offer standardized testing protocols so that they can convince hospitals of their safety and efficacy and make the most of their massive potential.”



Noel Nevshehir


If there was ever an industry in dire need of increased efficiency, cost containment and improved outcomes, health care tops the list. Despite consuming 18 percent of our nation’s GDP—equal to $3.4 trillion in annual expenditures—it is responsible for nearly 250,000 deaths due to medical errors, poor record keeping and a dismal lack of shared data among doctors about patients in their care.

From blockchain technology to surgical robots, medical experts worldwide agree that big data and artificial intelligence (AI) will play a key role in vastly improving health care quality and delivery. Aided by advances in sensor capabilities, computational power and algorithmic ingenuity, the pace of medical innovation is accelerating rapidly. 

To be sure, AI and big data are not the next best thing, they are here and now. Digital medicine is currently tracking down and destroying mutant cancer cells faster than ever before. It is also commonly used in operating rooms by doctors tapping into pools of data accumulated from previous surgeries to receive guidance from computers systems that have analyzed learned procedures that can be scaled up in order to make appropriate recommendations before, during and after treatment. So instead of depending on one or two local practitioners determining the course of lifesaving treatments, patients now have access to a knowledge base of thousands of doctors worldwide.

AI—versus natural intelligence used by humans to power up their brains—is akin to a jigsaw puzzle that deploys algorithms to draw together seemingly unrelated dots of information to paint a clear picture of the underlying data. It has changed dramatically since the concept was first introduced at Dartmouth College in 1956. Today’s man and machine AI is being aided by neural networks and deep machine learning methodologies powered by quantum computers and sophisticated algorithms that can crunch raw data into meaningful and actionable analyses.

A recent Wall Street Journal article titled “The Operating Room of the Future” is a case in point. Verb Surgical Inc., a recent startup formed by a partnership between Alphabet and Johnson & Johnson, is designing neural networks which enables robots to learn from one another by connecting each of them to the Internet to create machine-learning algorithms. Called “Surgery 4.0," it is the next logical step after traditional open procedures, minimally invasive surgery and the introduction of robotics. Using machine learning methodologies, computer programs study past procedures to identify best practices and potential errors. Verb’s technology has rendered the da Vinci surgical robot ancient by today’s standards despite the latter performing more than five million surgeries worldwide since 2000.

Another area ripe for AI is mental health. Researchers are developing new drugs and pharmaceutical combinations using machine learning to assess chemical reactions of anti-depressants among individual patients. They then tailor them to closely match an individual’s unique biochemical makeup. The results thus far are promising. In addition to detecting when a patient is veering off into a bipolar episode even better than a psychiatrist could ever imagine, these drugs are mitigating some of the wrenching side effects associated with traditional serotonin re-uptake inhibitors. Taken one step further, Stanford University has created chatbots to combat this debilitating disease. Patients feeling an aura can tell their chatbot how they are feeling that day. Using predictive analytics, the bot can quickly suggest coping strategies drawn from numerous cognitive behavioral therapies. Again, the results are impressive, reducing depressive symptoms by 20 percent.        

AI and big data can also predict patient falls resulting in head traumas, bone fractures and other injuries costing on average $30,000 per incident. Businessweek recently featured California-based Qventus, a company that developed a program to help nurses overcome alarm fatigue and sensory overload from the constant beeping sounds and alerts found in hospital environments. In many cases, this results in medical staff missing critical and life-threatening alarms altogether. Qventus’ software extracts and analyzes data to recognize patterns from call lights, bed alarms, electronic medical records, patients’ prescriptions and age and other fall indicators. In turn, this has reduced injuries by 13.5 percent.

Today there is no such thing as TMI when it comes to data capture now that we have the tools to make sense of it all. We are far from Star Trek’s tricorder ability to instantly detect what ails us, but we are moving in that direction. Even in its embryonic stage, AI outperforms dermatologists in spotting skin cancer, helps pharmacists predict more effective drug combinations, and spots nuances on x-rays far better than radiologists. 

Quantum computers uncovering newfound data have provided medical professionals with keen insights into disease mapping and prevention, rendered speedier diagnoses and treatments for patients, accelerated scientific discovery aimed at curing the leading causes of death in our country, and have also played a major role in predictive analyses and detection. 

Last month, Oakridge National Laboratory rolled out the world’s most powerful supercomputer, Summit, capable of 122.3 petaflops (or 200 quadrillion) calculations per second. Comparatively speaking, the human brain clocks in at 10-100 petaflops per second. However, computers do not yet match the human brain in areas like reasoning, perceiving and intuition. AI will never replace humans or lead us to the dreaded robopocclypse of lore. Considered by many as an idiot savant, AI is well versed in single, closely supervised tasks but out of its element performing wider, more complex calculations. Equally important, big data and AI are only as good as the data fed to it by their mere mortals (you and me) whom program neural networks and plug and play algorithms that run the risk of being inherently biased or, worse yet, a victim of groupthink.  

Yet, the opportunities that big data and AI present in vastly improving health care and the quality of life for ailing patients far outweigh the challenges. Together, man and machine are teaming up to exploit unprecedented amounts of medical information churned out by powerful computers and advances in integrated software technologies. According to Kevin Lasser, CEO of JEMS Telehealth, “we are at an inflection point now and will soon look back and realize that today was only the beginning of a major revolution in medicine.

Tina Reed


When physicians get the right kind of alert in an electronic health record—and actually follow its recommendation—it could result in fewer complications and lower costs among hospitalized patients, according to a new study.

Published in the American Journal of Managed Care, researchers from Cedars-Sinai Medical Center and Optum Advisory Services teamed up to examine alerts that popped up on physician computer screens inside their EHR system when their care instructions deviated from evidence-based guidelines.

Those alerts were based on the 'Choosing Wisely' initiative in which different specialties have identified common tests and procedures in their respective areas of expertise that may not benefit patients and should be avoided.

Looking at nearly 26,500 inpatient admissions at Cedars-Sinai Medical Center between October 2013 and July 2016, the researchers studied whether the treating physician followed either all or none of the Choosing Wisely guidance. In 6% of visits, physicians followed all of the triggered alerts and in 94% of visits, physicians followed none of the alerts. In particular, they examined data in which one more of the 18 most frequent alerts were triggered.

For patients whose physicians did not follow the alerts, the odds of complications increased by 29% and the risk of readmissions within 30 days of the patients' original visit was 14% higher. There was also a 6.2% increased length of stay and an additional 7.3% or more than $900 per patient increase in costs after adjusting in differences in patient complexity. 

To be clear, the study was examining a product created by Stanson Health, a spinout company founded by Cedars-Sinai executives and in which the health system is a major shareholder.

Optum is a licensed reseller of Stanson's Health's Choosing Wisely alert content and, for the study, married Stanson's data with its own analytics on cost and complications and length of stay for those same admissions.

“We said, ‘Hey, let’s look and see if we can demonstrate that we’re not only able to get docs to look at alerts but we can actually change the ultimate outcomes we’re trying to change like complications and readmissions," John Kontor, M.D., an executive vice president at Clinovations within Optum told FierceHealthcare in an interview. "For us, it was risk-free but it was very risky for Stanson."

It worked out by showing the association they were looking for, he said.

But there are limitations to the study, they acknowledge.

There was no way to measure the impact of specific alerts on outcomes to see if one was more significant than others. There is also no way to know if providers who were more likely to follow alerts may also be more engaged around quality and efficiency in how they practice day to day, Kontor said.

More study is needed to more closely examine the direct impact of the alerts, officials said. But, they said, the study shows using tools could have a real impact on costs and quality.

"The next step is to look at the characteristics of overall alerts and say 'We see a positive impact. Now let's look at what’s most effective about clinical decision support in order to maximize the positive impact on both for patients and providers," said Anne Wellington, a co-author of the study and managing director of the Cedars-Sinai accelerator.



Kate Monica 


The state’s emergency department care coordination program enables collaboration between hospitals via a statewide HIE.

Virginia recently launched its emergency department care coordination (EDCC) program to connect all emergency departments in the Commonwealth for streamlined communication and collaboration across healthcare facilities.

The program utilizes a connection to Virginia’s statewide health information exchange (HIE) —ConnectVirginia — to enable health data exchange between healthcare providers, health plans, and care teams for patients receiving emergency services.

Additionally, the program integrates directly into the state’s prescription drug monitoring program (PDMP) and advance healthcare directive registry.

Enabling a connection to the state’s PDMP will equip care teams with patients’ comprehensive medication histories to promote safer prescribing practices in Virginia’s emergency departments and reduce opioid-related deaths.

“Virginia continues to be at the forefront of health care innovation, and the ED Care Coordination Program marks an important step forward in making sure Virginians in every part of the Commonwealth have access to the highest quality of care,” said Virginia Governor Ralph Northam.

“With this secure technology, we can provide emergency medical personnel with access to a patient’s critical medical information in a timely way, which will increase effective and efficient care, avoid duplicative tests, reduce unnecessary costs, and improve health outcomes,” Northam continued.

The Virginia General Assembly first established the EDCC program in 2017 within the Virginia Department of Health (VDH). The program was made possible through collabroations between health systems, health plans, physicians, VDH, the Department of Medical Assistance Services, and the Department of Health Professions.

“The ED Care Coordination Program will help ensure appropriate care in the appropriate setting for patients, while also ensuring that their personal health information is secure,” said State Health Commissioner M. Norman Oliver, MD. 

The program is headed by VDH. Health IT services provider Collective Medical assists in facilitating health data exchange for ConnectVirginia to enable a connection between emergency departments.

“This program can offer peace of mind to patients and health care providers alike. I applaud the countless individuals who have worked collaboratively to make this program a reality, which helps safeguard the health and well-being of all people in Virginia and moves the state closer to becoming the healthiest state in the nation,” Oliver stated.

Looking ahead, the State Employee Health Plan and non-ERISA commercial and Medicare health plans operating within the Commonwealth intend to join the EDCC program by June 30, 2019.

The EDCC program will also expand to include other providers including primary care physicians, case managers, nursing homes, community service boards, private behavioral health providers, and Federally Qualified Health Centers (FQHCs).

These entities will have the opportunity to receive alerts and contribute patient health information to the exchange for improved care coordination.

States across the country are increasingly making efforts to improve health data access and exchange for emergency services providers for improved care coordination.

In May, the North Dakota Department of Health’s (NDHS) Division of Emergency Medical Systems launched an initiative to allow emergency medical services (EMS) personnel to engage in EHR use during patient transport.

The state health department partnered with EMS, fire department, and hospital health IT software company ESO to develop a clinical data repository designed to collect and analyze EMS patient care reports.

Enabling providers to collect and analyze patient health data helps to improve patient injury prevention efforts, performance improvement, and patient health outcomes.

 “Smart data and insightful analytics can be a real game changer for organizations across the healthcare spectrum when it comes to patient transport and treatment,” said ESO Healthcare Vice President Allen Johnson.

North Dakota joins California, Colorado, Indiana, Oklahoma, New York, and other states that have taken steps to improve EHR use and health data access for emergency services organizations.

Kate Monica 


Using EHR data, researchers were able to predict short-term mortality for chemotherapy patients through machine learning.

Combining the power of EHR data with machine learning may be the key to more accurately predicting mortality among cancer patients undergoing chemotherapy.

This finding comes from a recent JAMA study by Elfiky et al. that explored the effectiveness of applying machine learning to EHR data to predict patient’s short term risk of death when they start chemotherapy.

While chemotherapy significantly lowers the risk of recurrence in early-stage cancers and can improve survival rates and symptoms in later-stage disease, the treatment is challenging and costly for patients.

“These patients experience burdensome symptoms without many of the potential benefits of chemotherapy,” wrote researchers in the report.

Researchers set out to find a way to more accurately predict mortality risk before administering chemotherapy treatment to ensure patients that undergo the stress and burden of treatment will also reap its benefits.

In the cohort study, researchers analyzed the EHR data of 26,946 patients starting 51,774 chemotherapy regimens at Dana Farber/Brigham and Women’s Cancer Center from January 1, 2004 to December 31, 2014. Researchers identified the date of death for patients by linking their health records to their Social Security data.

The team classified patients by primary cancer and presence of distant-stage disease using registry data codes for metastases. With this information, researchers attempted to accurately predict death within 30 days of starting chemotherapy with a machine learning model based on single-center EHR data.

Ultimately, the machine learning model was able to accurately predict mortality rates despite lacking genetic sequencing data, cancer-specific biomarkers, or any detailed information beyond EHR data. Specifically, patient EHR data used in the machine learning model including symptoms, comorbidities, prescribed medications, and diagnostic tests.

“Mortality estimates were accurate for chemotherapy regimens with palliative and curative intent, for patients with early- and distant-stage cancer, and for patients treated with clinical trial regimens introduced in years after the model was trained,” stated researchers.

Researchers emphasized that EHR data contains “surprising amounts of signal for predicting key outcomes in patients with cancer.”

In addition to proving accurate, the machine learning model developed by researchers would also only minimally increase administrative burden on clinicians. The machine learning algorithm would not require manual data input from clinicians.

Instead, the algorithm could pull directly from existing patient EHRs.

“Although our algorithm was developed using a single institution’s data, its inputs are available nearly everywhere with an EHR,” wrote researchers.

“In addition, no special infrastructure is required to pull these data from an institution’s data warehouse; in the same way that today’s EHR systems pull a rich set of data from a database to present it to clinicians, an algorithm could pull and process the same data in real time using the processing power on a desktop computer,” the team continued.

The team also suggested the machine learning algorithm could potentially be designed to support EHR integration. Healthcare organizations could integrate the algorithm directly into existing health IT systems. 

“Algorithmic predictions such as ours could be useful at several points along the care continuum,” wrote researchers. “They could provide accurate predictions of mortality risk to a clinician or foster shared decision making between the patient and clinician.”

By predicting short-term mortality for cancer patients, clinicians can identify patients who are unlikely to benefit from chemotherapy and instead may be better suited for early palliative care referral and advance care planning.

“For patients receiving systemic chemotherapy, an estimate of 30-day mortality risk may be a useful quality indicator of avoidable treatment-associated harm,” researchers concluded.

Leveraging EHR data to predict patient health outcomes may help providers to avoid clinical decisions that add unnecessary strain on patients for minimal benefit.

Kate Monica 


ECRI recently outlined three ways healthcare organizations can improve health IT use to reduce delayed, missed, and incorrect diagnoses.

Improving communication between providers, diagnostic testing and medication tracking, and documentation through health IT use can help to reduce delayed, missed, and incorrect diagnoses.

This set of safe health IT use recommendations was released as part of ECRI’s Partnership for Health IT Patient Safety collaborative, which was established in 2014. The multi-stakeholder partnership is open to participation from providers, health IT companies, professional organizations, and other industry insiders.

The partnership’s most recent workgroup focused on improving patient safety during diagnostic testing, test tracking, and medication changes. Chaired by Vanderbilt University professor of pediatrics and biomedical informatics professor Christoph Lehmann, MD, the group identified three strategies healthcare organizations should keep in mind when implementing health IT solutions.

In combination, these three strategies can be effective in closing the loop of patient diagnoses.

“The goal of this Partnership workgroup was to look for technology solutions that all stakeholders could implement to close the loop — the tools provided here will help to do just that,” said ECRI Institute Program Director Lorraine Possanza.

First, the workgroup emphasized the importance of developing and applying health IT solutions that will communicate the necessary information to the correct members of a patient’s care team at the right time.

“Improve the transmission of information using standards for the formatting of normal, critical, abnormal-noncritical, and abnormal results,” wrote members of the workgroup in the report.

Efficient and effective communication between testing facilities, pharmacies, providers, and patients can enhance patient care across care settings, ECRI suggested.

“Designing, testing, deploying, and implementing health IT solutions to improve these communication pathways has the potential to make closing the loop a seamless and elegant process, with all diagnostic results and medications communicated to the provider, the pharmacy, and the patient,” wrote the workgroup.

ECRI also emphasized the importance of tracking diagnostic results and medication changes.

“Tracking of diagnostic results and medication changes is a time-consuming, burdensome task, but necessary to ensure a closed loop,” noted report authors. “Identification of interruptions and potential failure points in the process is critical to find and react to failures to close the loop.”

The workgroup recommended healthcare organizations identify where health IT can be useful for resolving deficiencies and improving medication and test result tracking.

Using EHR functionality to track diagnostic testing and medication changes may also be helpful. Furthermore, implementing lab standards — such as LOINC — may help to automate accurate matching of results and ordered tests to close loops.

Finally, ECRI suggested healthcare organizations use health IT to link, acknowledge, and document the review of information and action taken.

“This step includes the actor reviewing and acknowledging or acting upon information,” clarified report authors.

Toward this end, the workgroup recommended healthcare organizations help to improve interoperability by integrating EHR systems and other health IT modules across the care continuum to facilitate communication and documentation across care settings.

“This is to facilitate communication and acknowledgment, including the use of application programming interfaces (APIs) to allow laboratory systems and hospitals to communicate, as well as the use of HL7 and fast healthcare interoperability resources (FHIR) to aggregate and merge patient data from separate data sources,” wrote the workgroup.

Developing EHR functionality such as diagnostic results notifications may help to promote communication, acknowledgement, and documentation of diagnostic testing, medication changes, and actions taken.

Implementing these health IT use practices and closing the loop will help to avoid delayed or missed diagnoses, which may lead to patient harm.

“These safe practice recommendations are a call to action,” maintained the workgroup. “Although the EHR and its technology components have the potential to facilitate timely follow-up across all healthcare settings, it may take regulatory efforts to make this possible.”

Jack Murtha


Digital behavioral interventions can cut down the kind of overprescribing that empowers antibiotic-resistant bacteria — superbugs — in a cost-effective manner, according to a new study.

The report, published this week in the Journal of General Internal Medicine, found that three behavioral economics interventions can reduce the number of inappropriate antibiotic prescriptions, increasing quality of life and decreasing antibiotic resistance and its associated costs.

Researchers from the University of Southern California (USC) Schaeffer Center for Health Policy and LA BioMed, a California-based innovation incubator, undertook the research to help curb a growing public health crisis. Antibiotic-resistant superbugs have infected more than 2 million people worldwide, in part because roughly half of American outpatient antibiotic prescriptions are unnecessary, according to the study.


“Healthcare needs more studies such as these, targeted to identify successful initiatives that are both cost-saving and life-saving,” said David Meyer, Ph.D., who leads LA BioMed.

For the study, researchers analyzed three interventions, including: 1) Suggested alternatives, which leverage digital clinical decision support tools to suggest treatments that don’t use antibiotics; 2) Accountable justification, which requires prescribers to justify their use of antibiotics in a patient’s electronic health record (EHR); and 3) Peer comparison, which entails sending emails to clinicians regarding how their prescribing rates stack up to their colleagues.

These three tactics had already proved effective in reducing the number of erroneous antibiotic prescriptions over an 18-month period.

So, USC and LA BioMed researchers designed a study using the 30-year Markov model, with inputs from the literature and U.S. Centers for Disease Control and Prevention surveillance data. They homed in on 45-year-old adults who had shown signs of acute respiratory infections, for which patients sometimes inappropriately receive antibiotic prescriptions.

Then providers received the three aforementioned digital tools, along with training on guidelines for treatments for acute respiratory infections.

Measuring discounted costs, quality-adjusted life years and cost-effectiveness, researchers found that the strategies were not only successful but also adept at cutting costs. The total cost of each training intervention was $178.21, while suggested alternatives ran $173.22, accountable justification ran $172.82 and peer comparison ran $172.52, according to the study. What’s more, the training group experienced 14.68 quality-adjusted life years, falling short of 14.73, 14.74 and 14.74 for the three groups that received behavioral economic interventions.

The study and others like it could have big consequences. Public health officials are growing increasingly concerned about the potential effects of antibiotic resistance. Some experts have claimed that the phenomenon could result in the next great outbreak. But, as this study suggests, part of the answer could lie in the EHR and a provider’s email account.

Jessica Kent


What are the best population health management strategies for addressing common chronic diseases?

Chronic diseases are among the most costly, prevalent, and avoidable ailments impacting population health.

Conditions such as diabetes, hypertension, and opioid addiction claim thousands of lives and billions of dollars each year. The Centers for Disease Control and Prevention (CDC) reports that chronic diseases account for seven of the top 10 causes of death in the US and consume 86 percent of the nation’s annual healthcare spending.

The increasing prevalence and rising costs of these conditions make chronic disease management one of healthcare’s most challenging and urgent endeavors.

Yet many healthcare professionals struggle to find the time, tools, and resources to meet the holistic needs of patients.

The close association between chronic disease and patients’ social determinants of health adds to the complexity of treating and preventing these disorders.

Providers must consider and address the conditions in which their patients live, work, and play, as well as their ability to exercise regularly and access healthy food, in order to effectively manage and deter chronic diseases.

To confront common chronic diseases and the many factors that contribute to them, stakeholders from across the healthcare continuum will need to develop population health strategies that will improve patient outcomes.

What are some of the most common chronic diseases affecting patients in the United States, and which population health strategies should healthcare stakeholders use to manage these conditions?

DIABETES

According to the CDC, over 29 million Americans are currently living with diabetes. Another 84 million are prediabetic, and even more may be undiagnosed and untreated. The condition also accounts for more than 20 percent of healthcare spending.

Diabetes risk is significantly tied to social and economic circumstances. It is more common among non-white populations, with black, Hispanic, and Native American populations experiencing the disease at much higher rates than whites.

Medication non-adherence is an issue that leads to additional complications for many diabetic patients, and it is also linked to non-clinical factors. A 2016 report from IMS Health found that nearly half of Medicare diabetic patients are unable to keep up with medication adherence due to limited financial resources, language barriers, and insufficient care access.

To increase medication adherence rates, providers can work to engage and educate patients about their medications by developing personalized adherence plans.  

Additionally, providers can coordinate with community, pharmacy, and public health resources to improve adherence rates.

A 2016 study demonstrated that medication adherence interventions that take place at  retail pharmacies can help patients stay on track with their therapies, reduce preventable hospitalizations, and reduce overall healthcare costs.

“Community pharmacists are uniquely positioned to help mitigate the high risk of medication discontinuation and improve adherence for patients initiating therapy because of their access to prescription refill information and frequent interactions with patients,” the study stated.

Managing diabetes goes beyond adhering to medications, however. Patients must also make healthy food and lifestyle choices and regularly check their glucose levels to maintain their health.

To ensure patients are on track with managing their diabetes, providers can engage patients with text messages and mHealth communications. Providers can use these tools to remind patients about upcoming appointments, ensure they are making healthier lifestyle choices, and keep them on track with blood sugar testing.

Healthcare payers can also employ this strategy and launch mHealth programs to improve diabetes treatment, as UnitedHealth Group recently did for its Medicaid Advantage members.

HYPERTENSION

One in every three American adults has hypertension, the CDC states. The condition is strongly correlated with other cardiovascular conditions, including heart disease and stroke, two of the leading causes of death in the US.

The condition is most commonly seen in non-Hispanic black males, and black individuals are twice as likely to die from the condition as whites are.

Improving hypertension rates will require a collaborative approach, according to the CDC.

“Using team-based care that includes the patient, primary care provider, and other health care providers is a recommended strategy to reduce and control blood pressure,” the organization notes.  

A number of health systems and community organizations have taken this approach, working to engage patients and deliver hypertension care directly to underserved populations across the country.

The University of Michigan Health System collaborated with Meijer pharmacies in 2016 to provide more accessible care to adults with hypertension, offering patients treatment and monitoring services in their own communities.

Additionally, researchers at Cedars-Sinai Medical Center recently enlistedover 50 barbershops in the LA area to offer blood pressure checks and pharmacist-led consultations to customers, aiming to enhance chronic disease management tools within the community.

The researchers found that hypertensive customers who met directly with pharmacists significantly lowered their blood pressure rates.

Organizations can also take an analytics-based approach to hypertension management, as Kaiser Permanente illustrated with its Hypertension Program Improvement Process.

Healthcare organizations can utilize clinical analytics and the EHR to create a registry of high-risk individuals who may benefit from lifestyle changes, as well as use clinical analytics algorithms to determine the best treatment methods for hypertension patients.

OPIOID ADDICTION

Opioid addiction is one of the nation’s biggest health crises. Providers can act as the first line of defense against opioid abuse, and organizations such as the FDA have considered addressing the epidemic with mandatory opioid education for all healthcare professionals.

Clinicians have a responsibility to recognize signs of opioid misuse in patients, prescribe alternate treatments, and prescribe opioids more judiciously to avoid long-term consequences.

Providers can utilize state Prescription Drug Monitoring Program (PDMP) data to determine if their patients may be abusing opioids. These programs have shown considerable promise in reducing unnecessary prescription rates and raising provider awareness about potential opioid misuse.

Opioid abuse is also significantly tied to social and economic circumstances, with those suffering from addiction often having deeply rooted social or mental health problems.

As a result, treating and managing this disease requires efforts not only from providers, but also from government officials and community organizations.  

Pennsylvania’s Opioid Data Dashboard, a government initiative to combat the opioid crisis, gives health officials, lawmakers, and the public access to real-time data to help identify trends for future community needs.

The dashboard also helps build predictive analytics models to deliver a comprehensive picture of the epidemic in Pennsylvania.

In addition, CMS recently released a document explaining how states can use telemedicine to treat Medicaid beneficiaries in rural or underserved areas struggling with opioid misuse.

The organization also recommended that states receive federal support to create shared electronic health plans between providers and patients, which would allow both parties to set goals for pain management regimens and counseling.

COPD AND ASTHMA

The CDC reports that nearly 15.7 million Americans have received a chronic obstructive pulmonary disease (COPD) diagnosis, while asthma affects about 25 million individuals in the US.

These chronic conditions cost the healthcare industry billions each year. They are also heavily associated with individuals’ environmental circumstances and are often exacerbated by exposure to air pollutants in the home and workplace.

Research has shown that public health officials can use EHR data from local hospitals to identify specific geographic areas where there is a high risk for asthma.

Once they have identified high-risk areas, public health officials can assess the air quality, and environmental inspectors can evaluate the hazards in the area.

Officials could also use EHR data to identify patients with severe asthma. By developing a registry of patients who are frequently admitted to the hospital for asthma, officials can flag those most in need of care coordination and individuals who might benefit from home visits.

Community care strategies that utilize sensor applications can significantly improve the health of patients with asthma and COPD, as well as identify the environmental factors that can affect patients’ quality of life.

A 2017 program in Louisville, Kentucky doubled the amount of symptom-free days for asthma and COPD patients by attaching a sensor directly to patients’ inhalers to track the number of puffs used per day, how many times patients experienced symptoms, and where they experienced those symptoms.

Nearly 82 percent of participants saw a decrease in inhaler use, while Louisville officials were able to identify high-risk areas and work to improve air quality in these places.

DEPRESSION AND OTHER MOOD DISORDERS

Between 2009 and 2012, depression affected 7.6 percent of Americans aged 12 and older. The mood disorder is more prevalent among minority and lower-income populations, and is also associated with higher rates of chronic disease.

Despite the correlation between mental illness and chronic conditions, only 30 percent of mentally ill patients are screened for chronic disease.

Integrated care delivery that considers a patient’s mental and physical health can significantly improve mental health outcomes and ensure these patients are receiving the care they need.

Organizations can place behavioral health and primary care providers in the same location to improve patient engagement, foster patient self-management, and address the social determinants of health.

Additionally, providers can use web-based risk assessment tools to stratify high-risk individuals and increase depression screenings for patients, particularly for those who are not often screened in traditional settings.

Providers can then deliver proactive, preventative care to these patients, and gather insights on the factors that most often contribute to depression and other mood disorders.

Chronic disease management is a challenging task that can be made easier by collaborative efforts from primary care providers, community organizations, and other healthcare stakeholders.

By working together to develop population health management strategies and manage and treat patients suffering from common chronic diseases, stakeholders can reduce and prevent the prevalence and cost of these conditions.


Tina Reed


An electronic medical record system is being credited with helping a public health system in Ohio reduce its opioid prescriptions for acute pain by more than 60% in the last 18 months.

Officials from Cleveland-based MetroHealth System said they also cut opioid prescriptions by 25% for chronic pain. In all, they estimate they cut opioid prescriptions by 3 million pills.

How'd they do it? Officials pointed to the alerts they set up in the EMR system.

In particular, those alerts for prescribers were set up to flag patients who may be at risk for addiction to guide them toward alternative drugs and lower doses. They also had an alert to add a prescription for the antidote drug Naloxone when prescribing opioids. That alert led to a 5,000% increase in Naloxone prescribing in the past three months.

Beyond the EMRs, officials said every provider that is licensed to prescribe narcotics is required to be trained in alternatives for pain relief and attend mandatory town hall meetings to identify processes and tools for safe opioid prescribing. The health system implemented a safe opioid prescribing simulation program to allow providers to practice difficult discussions with patients seeking opioids.

“We’ve been tackling the opioid epidemic for a long time. Not until recently, did we recognize that providers can do a lot more,” said Akram Boutros, M.D., president and CEO of MetroHealth.

The health system also opened a Pain & Healing Center, which includes alternative pain management therapies such as acupuncture, infusion therapy, reiki, pain management, neurology, psychology and psychiatry.

This comes a year after MetroHealth created an Office of Opioid Safety to focus on education, advocacy and treatment.

It's also part of a broader shift in MetroHealth's approach. In January, MetroHealth began transforming its campus to include far more green space, designing it as a "hospital in a park." At the time, officials said the change wasn't just about beautifying its campus but about incorporating more holistic healing strategies.

Frank Landman


Technology has changed communication and consumerism so fast that we barely notice it anymore. Less than a decade ago, most people couldn’t imagine things like smart homes and augmented reality. Now, they’re regular consumer products.

In healthcare, though, technology’s impact hasn’t been so subtle. As costs rise at nearly unbearable rates and patients become more involved in their care, new technologies play an increasingly vital role in helping healthcare organizations improve the quality of care they provide.

Patients, or the consumers in the healthcare industry, have benefited most from the industry’s technology race. From preventive treatments to virtual care and more effective disease management, these are just a few ways healthcare leaders can leverage tech to transform the industry:


1. Use data and machine learning to prevent catastrophe.

Data collection and machine learning have been two of the biggest disruptions in healthcare, and predictive analytics is the most notable reason why. By using AI to analyze data on patient and population health trends, providers can more accurately predict health crises and pinpoint their origins to prevent the spread of illness.

This tech was put into action last spring when Pariveda Solutions, a strategic services and information technology consulting company, and a Texas-based pediatric healthcare system used predictive analytics to help stop an infectious, hospital-borne disease that was spreading through the hospital at the time. They collected data on who cared for, saw and delivered medicine to the impacted children and tracked the movements of everyone on that list. In four weeks, predictive analytics determined the problem was central line-associated bloodstream infection (CLABSI), and the hospital was able to prevent it from spreading further

2. Make essential care more accessible with telemedicine, while decreasing data security risks.

For patients who experience mobility issues or who live in rural areas, telemedicine has provided a way to seek medical care more conveniently and given patients more choice in healthcare providers. They can schedule their appointments online with a laptop or mobile device, videoconference with providers to avoid unnecessary trips to the doctor’s office and even rely on internet-connected devices for remote health-related monitoring.

On the other hand, telemedicine may expose a healthcare organization and its patients to security risks involving HIPAA-protected information. Hackers and ransomware are rampant, so organizations must ensure their communications are protected. Fortunately, tech has stepped up to help with that as well, as evidenced by companies like Paubox, which specializes in a HIPAA-compliant email encryption service to protect against data breaches.

3. Provide more accurate, cost-effective treatment through electronic record keeping.

Since electronic medical records began to become standard in 2014, accuracy in medications and treatments throughout a patient’s care cycle has improved significantly. When patients move or must visit a specialist provider, EMRs are even more important in reducing errors. Computerized physician’s orders are easier to understand, patients’ compliance with those orders is easier to track and health conditions are being treated without wasting money on needless procedures.

According to an Institute of Medicine report, $210 billion is spent annually on unnecessary medical care each year. Fortunately, electronic patient records can play a role in reducing this waste. The positive effects of EMRs can be seen at Virginia Mason Medical Center, where an analysis of medical claims data found that high-cost treatments for conditions like back pain, headaches and sinus problems were being driven by expensive MRIs and CT scans, many of which were unneeded. Harvard Business Review reports that when the center embedded an evidence-based checklist for ordering advanced imaging into its EMRs, the use of these costly tests fell 25 percent.

4. Use video games and virtual reality to help with rehabilitation.

When video games become physically interactive with the Nintendo Wii, consumers realized that video games could actually help improve their physical health. Because the Wii’s controllers are operated with motion, games that involve movement — such as running, jumping, swinging a racket or rolling or throwing a ball — force players to get up and move to play. It didn’t take long for healthcare organizations to see the potential benefits of the Wii for everything from poststroke rehabilitation to promoting physical fitness in long-term care facilities.

Meanwhile, some in the medical field have envisioned benefits beyond getting exercise. A study published in the Journal of Geriatric Physical Therapy showed that bowling on the Wii helped elderly patients reduce their risks of falling and suffering an injury. Now, rehabilitation experts routinely use video games and virtual reality to help patients recover from serious injuries, cardiovascular disease, trauma and much more. The Creative Media and Behavioral Health Center at the University of Southern California, for instance, has found virtual reality and video games can help motivate patients who do at-home physical therapy.

Among industries increasing their tech use, healthcare has seen some of the most significant transformations from widely implementing advanced technology. The exciting thing is that technology continues to evolve, and with a firm grip on today’s leading advancements, the medical field is poised to transform even further.

Elizabeth Snell

The Healthcare Leadership Council’s roadmap addressing the opioid crisis states improved EHR integration regulatory requirements can help EPCS adoption.

Adopting electronic prescriptions for controlled substances (EPCS) through modern regulatory requirements, including improved EHR integration, is one critical solution to opioid abuse, according to the Healthcare Leadership Council’s (HLC) “Roadmap for Action.”

Having a national prescription drug monitoring program (PDMP) and better opioid stewardship and disposal were also key recommendations from the Council.

More than 70 healthcare organizations, including Surescripts and the National Association of Chain Drugstores have already announced their support of the roadmap.

“EPCS is just one tool in our arsenal to fight opioid abuse,” Surescripts said in a statement. “Robust, electronic medication history data is available nationwide across all care settings. Having an up-to-date view of a patient’s medication history at the point of prescribing empowers prescribers to make the best care decisions for their patients.”

HLC members include chief executives from numerous healthcare stakeholders, including but not limited to hospitals, health plans, pharmaceutical companies, medical device manufacturers, biotechnology firms, and health product distributors.


The council’s National Dialogue for Healthcare Innovation (NDHI) organized the Opioid Crisis Solutions Summit on May 14, 2018 in Washington, DC. The Roadmap was created based on discussions at the Summit.

The Roadmap highlighted five priority areas for healthcare leaders, policymakers, and regulators:

  • Improve healthcare system approaches to pain management
  • Improve healthcare system approaches to prevent opioid misuse
  • Expand access to evidence-based substance use disorder (SUD) treatment and behavioral health services
  • Promote improved care coordination through data access and analytics
  • Develop sustainable payment systems that support coordination and quality care

The Roadmap then discussed specific recommendations for healthcare leaders, policymakers, and regulators, with ten to 15 suggestions for each group.

The Drug Enforcement Administration (DEA) should modernize current regulatory requirements for EPCS. This could improve EHR integration and help reduce the extra cost and burden on healthcare providers.

CMS should “provide a secure electronic transmittal infrastructure that would facilitate electronic prior authorization in Medicare Advantage and Part D and move towards greater standardization and efficiency in the prior authorization process,” the Roadmap recommended.


Healthcare leaders should pledge to adopt e-prescribing for all controlled substances by 2020, and will also need to develop a plan for improving access to a range of evidence-based, non-opioid, opioid sparing, and non-pharmacological pain management therapies.

Healthcare leaders should also work to close the SUD treatment gap by working to increase access to appropriate in-person or telehealth SUD treatment and recovery services.

“Specific efforts could include organizational commitments to reducing care fragmentation, providing or incentivizing medication-assisted treatment (MAT) training in underserved areas, and investing in peer and recovery support workforce and services,” the Roadmap explained.

“While public policy has a vital role to play in removing barriers to advancements in care and empowering accelerated therapeutic innovation, private sector leadership is critical on every aspect of this issue, from improvements in pain management to data-driven proactive interventions to strengthened opioid stewardship,” Roadmap authors concluded.

“By building upon ongoing initiatives that are already yielding promising results, healthcare leaders can and will make a difference in stemming a crisis that has already claimed too many lives and damaged too many families and communities.”   


CMS released its own roadmap earlier this month, highlighting the need for interoperability and clinical data to help curb the current opioid crisis.

The agency explained that it would use health data to target prevention and treatment efforts and identify trends of fraud and abuse among patients.

“CMS is working to ensure that beneficiaries are not inadvertently put at risk of misuse by closely monitoring prescription opioid trends, strengthening controls at the time of opioid prescriptions, and encouraging healthcare providers to promote a range of safe and effective pain treatments, including alternatives to opioids,” CMS said in a June 2018 blog post. “We are also working on communications with beneficiaries to explain the risks of prescription opioids and how to safely dispose of them, so they are not misused by others.”

Leveraging health data to understand opioid use patterns across populations can be greatly beneficial, the agency explained. Additionally, that information can promote healthcare interoperability and health data exchange across the care continuum, helping monitor trends to assess the effectiveness of prevention and treatment solutions. 

“The roadmap is also a demonstration of CMS’ commitment to explore and offer viable options to address the crisis, to share the information we collect with other agencies and organizations, and to protect our beneficiaries and communities affected by the crisis,” CMS concluded.

Jessica Kent


A machine learning tool developed at Carnegie Mellon University uses big data from the electronic health record to accurately predict sepsis.

Researchers at Carnegie Mellon University’s (CMU) Heinz College are applying a machine learning algorithm to big data in the electronic health record (EHR) to more accurately predict sepsis, one of the most dangerous and insidious hospital acquired conditions.

The Sepsis Alliance reports that more than 1.7 million people in the US are diagnosed with sepsis annually. Of those affected by the condition, an estimated 270,000 die each year.

Sepsis is also the number one driver of hospital costs in the US, consumingmore than $27 billion annually. Many times, the infection is acquired in the community, and patients with complex comorbidities are often at the highest risk.

“The problem underlying sepsis is that it’s incredibly heterogeneous,” said Jeremy Weiss, MD, PhD, Assistant Professor of Health Informatics at CMU’s Heinz College, to HealthITAnalytics.com

“Anybody can get sepsis from a multitude of infections, at different sites, and with different comorbid profiles.”

Traditionally, providers identify high-risk sepsis patients by evaluating symptoms and medical histories.

However, Weiss and his team are working to improve the speed and accuracy of sepsis prediction.

Using machine learning and EHR data, Weiss has developed a method of accurately assigning risk scoresto patients, offering a way to catch sepsis earlier than is possible with standard processes.

“EHR data is very detailed. There’s a lot of time-stamped information,” Weiss said.

“A lot of classical analyses don’t get to capture that kind of information. With EHRs, where this data is automatically entered, we can look at the temporal progression of disease and update our risk models more adeptly.”

Weiss and his team utilize this time-stamped data to evaluate information such as blood tests, prescribed drugs, and blood pressure. This data is typically contained in the structured portion of the EHR, which they can access during a healthcare encounter and use to make real-time predictions.

“Data such as lab tests, prescribed medications, and vital signs will inform us when procedures were performed and the background set of diagnoses for the patients upon entry,” said Weiss.

To extract relevant patterns from structured EHR information, Weiss and his team are applying machine learning algorithms, which can analyze many different data points and assist in making accurate predictions, he said.

Weiss and his team are also exploring how clustering of similar cases can help to refine predictive analytics and get ahead of sepsis that may develop in future patients.  

“If we can identify clusters with very specific phenotypes, then we can tailor treatments to those subgroups,” said Weiss.

The Heinz College group is not the first to use machine learning algorithms and EHR data to accurately predict sepsis in patients.

Researchers at the University of Pennsylvania developed a machine learning tool that continuously monitored EHRs and identified patients headed for sepsis or septic shock a full 12 hours before the onset of the condition.

Additionally, researchers at North Carolina State University, in collaboration with Mayo Clinic and Christiana Care Health System in Delaware, have usedEHR data and machine learning to improve how the healthcare system identifies and treats patients with sepsis.

Weiss and his team hope to make similar strides in sepsis prediction and prevention.

“We’re trying to leverage much more information from the EHR to tailor simpler risk scores,” Weiss said.

“While those scores are a really good, quick check for what your general risk is, we’re really interested in having a much better risk estimate that will draw from a lot more signals.”