How an interdisciplinary kaizen group within CDC is charting a roadmap for future metrics to improve population health and provider satisfaction.
Addressing global infectious diseases has been an ongoing challenge. To tackle the issue, in 2018 the U.S. Department of Health and Human Services’ Centers for Disease Control and Prevention put together a Kaizen group consisting of an interdisciplinary collection of healthcare and IT professionals.
The group worked collaboratively to develop a roadmap and metrics for the future of clinical guidelines as they apply to electronic health records and infectious diseases.
Such an approach has several advantages — and a handful of drawbacks. That’s what attracted the attention of Steph Hoelscher, chief clinical analyst for the Office of Clinical Transformation at Texas Tech University Health Sciences Center’s School of Medicine in Lubbock, Texas.
Digitalizing these guidelines and algorithms would consist of creating them in a way that an EHR could accept them quickly from an outside source with minimum modification needed to the system.
“The goal of this would be to decrease guideline adoption time as well as improve both provider and informaticist satisfaction, not to mention improve overall population health,” said Hoelscher. “The process is still in its early stages and hopefully will move to larger scale testing within the next year.”
For the project, Hoelscher and her team looked to align their facilities with the CDC’s initiative, the Quadruple Aim, as well as the 21st Century Cures Act, in regards to clinician documentation burden.
She’ll discuss the implementation in more depth at the upcoming HIMSS19 annual conference in Orlando, Florida -- focusing on preparing an EHR for the future of clinical decision support, and bridging the gap until they get there.
“The process can be as complicated or simple as your development team allows for,” said Hoelscher. “Proper CDS development takes time, patience, evidence, subject matter experts committed to the project, and executive support.”
Facilities often lack the time and resources to properly develop a new process -- one that involves testing, reevaluation and maintenance. But for it to truly succeed, it has to be designed to stand the test of time, said Hoelscher.
That takes commitment, and with the constant changes of both medicine and technology, having CDS design that’s evidence-based, usable and safe can be a challenge.
“If you push hard for a strong CDS foundation, maintenance later on can be made much simpler,” said Hoelscher.
There are, of course, both pros and cons of clinical support in EHRs. First, the cons.
The limits of current technology and education are a big one. As fast as technology often moves, sometimes it’s just not fast enough; EHRs are often just not ready for the changes an organization may want to make today, and there have to be temporary bridges built in order to make it across the chasm.
And then there’s making a complex concept understandable to multiple levels of healthcare professionals.
“As with any maintenance cycle of a CDS project, quality education and often re-education needs to be a top priority,” said Hoelscher. “Staff and providers that do not ‘understand’ changes or new workflows, often succumb to frustration, and that’s what we are trying to avoid.”
Yet there are some pros as well. Hoelscher’s organization has integrated the potential for local disease detection into its EHR. With diseases like measles popping with some frequency as of late, it’s not enough to simply concentrate on Ebola and Zika.
“With that being said, an improved CDS process can possibly help you recognize the next Virus ‘X’ as well,” she said. “We are at the point where it’s not a matter of if, but when. If our systems can be enhanced enough to accept digitized algorithms from agencies such as the CDC in the future, the improvement in quicker detection and treatment of impacted patients could be profound, even life-saving.”
Hoelscher will share these thoughts and more at HIMSS19 annual conference in Orlando in a session entitled “Clinician Satisfaction: Digitalizing ID Clinical Guidelines,” at 3 p.m. on Tuesday, Feb. 12 in room W311E.
In order to grapple with spending increases, the U.S. healthcare industry is transforming the way physicians are compensated to provide care to patients from the current fee-for-service (FFS) model to one in which medical providers are paid a flat fee for servicing a defined group of patients.
This pivot in reimbursement is often discussed as far off. A survey by Numerof & Associates, a St. Louis, Missouri-based healthcare strategy consultancy, finds that most health organizations have been slow to make the shift: 54% receive less than 10% of their revenue from risk-based agreements.
However, as consumers become more sophisticated, and payers and health systems become more emboldened to wring out costs, we expect to see the shift in reimbursement models take off in the coming years. Provider groups that embrace this shift now have an opportunity to gain meaningful first-mover advantages.
Policy changes will further enhance the transition. Annual FFS pricing increases are set by the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), and physicians treating Medicare patients will receive FFS raises of 0.5% this year and next. Then, from 2020 through 2024, there will be no automatic payment increases.
That’s not a recipe for growing revenues. But instead of fearing the transition to a value-based compensation model, physician groups that have the foresight and inclination to harness analytic tools, patient data, and standardized processes will have a leg up on other practices, positioning them to win new contracts with payers and systems, and capture more patient volumes.
A key to a profitable shift away from the traditional FFS reimbursement model is a practice’s ability to effectively price a procedure based on their understanding of the patient’s medical history and other risk factors, as well as an understanding of what it costs to perform the procedure, including consumables, implants, and the time the physician spends with the patient. Automated or programmatic data analysis can help.
A good place to begin is an analysis of the top procedures the group performs. Using data from the practice’s electronic medical records system to answer the following questions can help providers determine a competitive price for these procedures.
As an example, an orthopedic group analyzing the pricing for a knee replacement might evaluate:
What is being spent on implants? Are there commonalities or preferences on implants across the group? If a standard implant system could be selected, the group could standardize procedures or timing, allowing it to negotiate for better prices with vendors and better price the time associated with the procedure.
What are the common complications associated with knee replacements? Are there recommendations that can be given to patients to reduce complications and hospital readmissions and improve outcomes?
What are the facility charges?
With this level of data in hand, a group can develop a series of costs for procedures and an appropriate target profit per patient before negotiating a capitated rate with a health system or insurance company.
Physician practices that are making the move to this model now by partnering with or serving a healthcare system or insurance provider are significantly growing their businesses. In doing so, they exchange the potential upside of being able to charge for extra services for the ability to gain market share by treating significantly more patients.
Groups that do not have this level of sophistication or understanding of their data or how to deliver care in a cost-effective manner will be at a profound disadvantage and will likely miss the opportunity to participate in ACOs or narrow provider networks.
All this requires significant analysis of data that the practice can draw from its electronic health records system. Some practices will be able to undertake this analysis with the leadership and guidance of a controller and a billing team, while others may need assistance from a consultant. Currently, numerous data-focused healthcare information service businesses are helping physician groups sift through their patient data, procedure outcomes, and expenses to effectively price their services.
This approach can deliver better healthcare to patients at a lower price while helping efficient practices grow significantly. By using data and analytics in this way, healthcare providers and insurers have the same incentive to lower healthcare costs. We’re already seeing this approach work.
Kaiser Permanente, for example, owns both its own insurance and health systems, in which 95% of its 12.2 million members are covered on a capitated basis. All manner of practices can benefit from this approach, from dermatologists, orthopedic surgeons, gastroenterologists and ophthalmologists, to name just a few.
The Harvard Business Review writes that the value-based approach can trim waste from U.S. healthcare spending while also making physicians’ practices significantly more profitable: “Better products at lower costs generate higher value, which helps organizations achieve better market positions. Strategies based on that thinking have transformed other industries. We believe that they will do the same in healthcare. Population-based payment will play a critical role in helping care delivery groups make that leap.”
Whether physicians’ practices like it or not, this transition is taking place. It may take the next 15 to 20 years to get there, but it’s always good to be ahead of the curve.
The All of Us Research Program, part of the National Institutes of Health (NIH), has launched the Fitbit Bring-Your-Own-Device (BYOD) project. Now, in addition to providing health information through surveys, electronic health records, and bio-samples, participants can choose to share data from their Fitbit accounts to help researchers make discoveries.
According to All of Us research program officials, the project is a key step for the program in integrating digital health technologies for data collection.
The All of Us Research Program, established by the White House in 2015, aims to advance precision medicine by studying the health data of 1 million diverse Americans over the next five years. One aim of the project is to include groups that have been historically underrepresented in research. As of September 2018, more than 110,000 people have registered with the program to begin the participant journey, and more than 60,000 have completed all elements of the core protocol.
The participants are sharing different types of information, including through surveys, access to their electronic health records and blood and urine samples. These data, stripped of obvious identifiers, will be accessible to researchers, whose findings may lead to more tailored treatments and prevention strategies in the future, according to program officials.
Digital health technologies, like mobile apps and wearable devices, can gather data outside of a hospital or clinic. This data includes information about physical activity, sleep, weight, heart rate, nutrition, and water intake, which can give researchers a more complete picture of participants’ health.” The All of Us Research Program is now gathering this data in addition to surveys, electronic health record information, physical measurements, and blood and urine samples, working to make the All of Usresource one of the largest and most diverse data sets of its kind for health research,” NIH officials said.
“Collecting real-world, real-time data through digital technologies will become a fundamental part of the program,” Eric Dishman, director of the All of Us Research Program, said in a statement. “This information, in combination with many other data types, will give us an unprecedented ability to better understand the impact of lifestyle and environment on health outcomes and, ultimately, develop better strategies for keeping people healthy in a very precise, individualized way.”
All of Us participants with any Fitbit device who wish to share Fitbit data with the program may log on to the All of Us participant portal at https://participant.joinallofus.organd visit the Sync Apps & Devices tab. Participants without Fitbit devices may also take part if they choose, by creating a free Fitbit account online and manually adding information to share with the program.
All of Us is developing additional plans to incorporate digital health technologies. A second project with Fitbit is expected to launch later in the year, NIH officials said, and this project will include providing devices to a limited number of All of Us participants who will be randomly invited to take part, to enable them to share wearable data with the program.
The All of Us research program plans to add connections to other devices and apps in the future to further expand data collection efforts and engage participants in new ways.
The University of Chicago Medicine was able to adjust hospital EHR use and educate doctors and nurses on how to reduce in-hospital sleep deprivation, thereby improving sleep for patients staying at the hospital.
The changes were part of a study designed by UChicago Medicine researchers called Sleep for Inpatients: Empowering Staff to Act (SIESTA), which examined the effects of nighttime sleep interruptions on patients in the hospital environment and how to improve patient sleep.
For the study, SIESTA employed electronic “nudges” using the patients EHR to urge doctors and nurses to avoid sleep disruptions that have minimal value, such as waking patients at night to take vital signs or administering nonurgent medications.
“Efforts to improve patients’ sleep are not new, but they do not often stick because they rely on staff to remember to implement the changes,” said Dr Vineet Arora, a professor of medicine at the University of Chicago and the study’s lead author.
For the study, the researchers interviewed patients about sleep barriers. During the interviews, the researchers found that major barriers to sleep were taking vital signs, administering medications, and drawing blood during sleep hours.
The researchers also found out that doctors did not know how to change the default vital signs order for every four hours or how to batch-order morning blood draws at a time other than 4 am.
Based on the interviews, the researchers developed and integrated electronic nudges into the EHR and taught doctors and nurses about the sleep friendly tools in the system. Taken together, these changes reduced the number of unnecessary sleep interruptions.
The researchers published the results of the study in the January 2019 issue of the Journal of Hospital Medicine.
The one-year study focused on two 18-bed general medicine units at UChicago Medicine. Between March 2015 and March 2016, 1,083 patients were admitted either to the SIESTA-enhanced unit or to a standard hospital unit nearby.
Both units had doctors who were trained in the use of nighttime orders, but only the SIESTA unit had nurses who were trained to advocate for patients with the doctors.
For the SIESTA unit, decisions by doctors and nurses not to take vital signs every four hours increased from 4 percent to 34 percent and sleep friendly timing of medication administration rose from 15 percent to 42 percent. Nighttime room entry decreased by 44 percent.
For the standard unit, decisions not to take vital signs every four hours increased from 3 percent to 22 percent, sleep friendly timing of medication administration increased from 12 percent to 28 percent.
As a result, patients in the SIESTA unit had six fewer nighttime room entries, four times fewer sleep disruptions for medication administration, and three times fewer sleep disruptions for vital signs.
The researchers concluded that adjustments to the EHR system along with doctor and nursing education significantly reduced the number of nighttime vital sign orders and led to better timing of nighttime administration of medications in both units.
However, the study found that having the nurses as patient champions helped to sustain the benefits of a sleep friendly environment in the SIESTA unit over time.
Sara Ringer, a patient in the SIESTA unit, said that the changes enabled her to sleep more soundly. “As a frequently hospitalized patient, I am used to being woken up as often as every 1 to 2 hours. It never feels like your body has a chance to rest and heal. My last hospitalization at University of Chicago was one of the easiest I've had because the hospital staff made it possible for me to sleep.”
“This illustrates the importance of engaging both nurses and physicians to create sleep-friendly environments in hospitals,” concluded Arora.
The research was funded by the National Institute on Aging and the National Heart, Lung and Blood Institute.
Simple intervention cut monitoring time by 17% in randomized trial
Electronic health record (EHR) alerts when a telemetry order exceeds the recommended duration contributed to a safe decline in cardiac monitoring in a cluster-randomized clinical trial.
The EHR notification cut telemetry monitoring by 8.7 hours per hospitalization compared with no notification (P=0.001), and there wasn't a significant variation in emergency calls (6.0% vs 5.6%, P=0.90) or urgent medical events between groups, reported Nader Najafi, MD, of the University of California San Francisco, and colleagues in JAMA Internal Medicine.
The effect on telemetry duration was "notably smaller" than seen in other multicomponent quality improvement interventions, Najafi's group wrote.
However, it "was achieved without a concomitant educational or audit and feedback campaign, without human resources dedicated to monitoring telemetry use, and without an increase in adverse events as measured by rapid-response or medical emergency activation," they noted, so it would be "less costly and more scalable."
The study assessed 1,021 patients. The intervention group had a mean age of 64.5 and were 45% women, while the control group had a mean age of 63.8 and were 46% women.
The 12 general medicine service health teams, four of which were hospitalist teams and eight of which were house-staff teams, were cluster randomized at the team level to get or not get pop-up alerts on their computer screen during order entry in daytime hours when a patient had an active telemetry order outside the ICU that didn't meet the American Heart Association indication-specific best practice standards (with a few local tweaks).
When physicians received a telemetry notification, they decided to stop telemetry monitoring 62% of the time, 7% of the time they disregarded the notification, 21% of the time they requested telemetry again, and 11% of the time physicians responded to the alert but continued with the current course, the investigators found.
The mean telemetry hours per hospitalization were 41.3 with the intervention versus 50.0 among controls, a reduction of 17%.
The investigators acknowledged the limitations of their work: The results might not generalize to other locations, as the study is based on a single medical facility. And, the suggestions for telemetry hours were partially based on local expert outlook, making them more lenient than national practice guidelines.
"Finally, the preintervention mean telemetry hours at the UCSF Medical Center general medicine service was already lower than the baseline in prior studies,which may have limited the effect size of this intervention," the researchers wrote.
Recurrent neural networks (RNN) provided significantly better accuracy levels than the clinical reference tool in predicting severe complications during critical care after cardiothoracic surgery, a new study found.
Alexander Meyer, M.D., department of cardiothoracic and vascular surgery at German Heart Center Berlin, and his team used deep learning methods to predict several severe complications — mortality, renal failure with a need for renal replacement therapy and postoperative bleeding leading to operative revision — in post-cardiosurgical care in real time.
“For all tasks, the RNN approach provided significantly better accuracy levels than the respective clinical reference tool,” the researchers wrote.
Mortality was the most accurately predicted, scoring a 90 percent positive predictive value (PPV) and an 85 percent sensitivity score. Renal failure had an 87 percent PPV and 94 percent sensitivity score.
The deep machine learning method also showed area under the curve scores that surpassed clinical reference tools, especially soon after admission.
Of the data studied, postoperative bleeding was the most difficult method to predict, due to how accurate the predictions were for mortality and renal failure. Postoperative bleeding had a PPV of 87 percent and sensitivity of 74 percent.
The team studied electronic health record (EHR) data from 11,492 adults over the age of 18 years old who had undergone major open-heart surgery from January 2000 through December 2016 in a German tertiary care center for cardiovascular diseases.
Patients’ data sets were studied for the 24 hours after the initial study, and if any complication occurred, patients were labeled accordingly.
Researchers measured the accuracy and timeliness of the deep learning model’s forecasts and compared predictive quality to established standard-of-care clinical reference tools.
Meyer told Healthcare Analytics News™ that one of the major findings of this study was that the system developed outperformed all three pre-existing benchmarks. He added that it is possible to work on a real-time uncurated clinical data stream.
With this information, physicians in emergency care units can perform interventions immediately if a patient is experience complications.
“Health systems should openly embrace this technology and ideally try to make use of it,” Meyer said.
At the very least, health systems can try to get regulations and developments so that this technology can be used.
In a clinical setting, technology like this is difficult to implement and generally demands a financial incentive.
Hospitals can work with researchers and companies to push this technology forward and gain support from politicians to help provide financial means and ways to attain these tools.
Case Western Reserve University
Electronic health records (EHRs) produce savings for hospitals by reducing the average length of patient stays—but only in facilities meeting the highest federal standards for implementing the technology, according to new research from Case Western Reserve University.
The findings are significant for a health-care industry with growing levels of spending—now roughly 18 percent of the nation’s gross domestic product.
In hospitals meeting the federal government’s measure of “meaningful use” of electronic health records, patients are discharged nearly four hours earlier—approximately a 3 percent reduction of the average five-day hospital stay.
For sicker patients, the benefit was even greater: Those with complex or multiple chronic conditions see up to an additional 0.5 percent reduction in their hospital stays.
What’s more, researchers found that these shortened stays did not come with an increase in re-admissions. With prolonged patient stays costing hospitals an average of $600 a day, the use of electronic records could help contain growing costs, especially amid a trend of reduced reimbursements from insurance companies and entitlement programs.
“Longer hospitals stays cost more money for all involved,” said Manoj Malhotra, dean of the Weatherhead School of Management at Case Western Reserve and co-author of the research.
“Electronic health records, when meaningfully implemented help patients go home sooner, reducing their exposure to germs in the hospital and likelihood of having to come back," he said.
Hospitals that did not fully engage in meaningful use of electronic records showed no significant reductions in length of patient stays, according to the study, which was published in the Journal of Operations Management.
“Any efficiencies, even small improvements, can produce significant savings when adopted in a large health-care system—and are certainly preferable to the alternative,” said Malhotra, who is also the Albert J. Weatherhead, III Professor of Management at the university.
Health-care savings, thanks to federal perks (and penalties)
While electronic records are touted for their potential to reduce hospital errors and inefficiencies, their adoption had been slow among U.S. hospitals.
In 2010, a $27 billion package included in the Health Information Technology for Economic and Clinical Health (HITECH) Act encouraged hospitals to adopt and meaningfully use the technology—and established penalties for failing to do so, such as negative adjustments to Medicare and Medicaid reimbursement.
The approach has been successful in pushing increased adoption of electronic records: By 2015, the level of adoption had reached 80 percent of hospitals nationally. But a more proactive approach that meaningfully uses the technology beyond mere adoption may be needed to see more progress, researchers conclude.
The researchers categorized hospitals into one of three categories—partial adoption of EHRs, full adoption of EHRs and “meaningful assimilation” of EHRs.
“Whereas partial or full adoption showed no benefits for reducing patient stays, meeting the government’s highest standard of meaningful use reduced length of stay without any adverse impact on readmissions,” said Malhotra. “Results from this study indicate that meaningful assimilation of technology is likely to help free-up clinicians and other valuable resources –this approach is preferable to making additional investments in facilities or hiring additional employees as more people seek hospital services.”
The research—co-conducted with Deepa Wani, an assistant professor of management science and statistics at the University of Texas at San Antonio—used four years of detailed patient-level data from all acute-care hospitals in California, in addition to as data reported by the Centers for Medicare & Medicaid Services (part of the U.S. Department of Health and Human Services) on hospitals that successfully attested to meaningful use criteria stated in HITECH.
Austin Fitzgerald / U. of Missouri
Nursing homes that adopt more sophisticated information technologies are seeing specific improvements in the quality of care, a new study shows.
These improvements include significant decreases in urinary tract infections, patients reporting moderate to severe pain, and patients with new or worsened pressure ulcers.
Health care providers in hospitals and ambulatory care are currently incentivized with federal funds to adopt health information technology (IT). Nursing homes, however, have been largely left out of these incentive programs, although this health care sector is beginning to see some benefits. For example, IT systems support health information exchange and access to electronic health records by care providers across settings, enabling them to address patients’ needs better.
Now, a new study has linked more sophisticated nursing home information technology, including electronic medical records and other digital data systems in resident care, clinical support, and administrative activities with specific improvements in quality.
“We already knew that information technology can help create better care outcomes, but this study helped us see which technologies improve which elements of care,” says Gregory Alexander, a professor of clinical informatics at Missouri University. “As IT capabilities and extent of IT use improved in nursing homes, we saw an associated decline in urinary tract infections, among other correlations.”
Alexander and his colleagues collected surveys once per year for two years from nursing homes nationwide. The researchers compared the responses, which rated the sophistication of a given facility’s information technology, against federal data describing 18 quality measures in those same facilities, and technology had positive affects on quality of care. For example:
The researchers also observed that while the overall trend was an increase in IT adoption, some nursing homes actually lost capabilities between years one and two. Though these facilities were outliers, Alexander says they reflect the challenges nursing homes face when adopting new technology.
“Federal incentive funds are going into hospitals and ambulatory care, not nursing homes,” Alexander says. “Many homes don’t have a trained expert to manage the technology, so even if they do decide to upgrade their IT capabilities, they may abandon certain ones because they are too difficult or expensive to manage. If they aren’t being reimbursed for investing in information technology, they may decide it isn’t worth the time and money.”
Alexander says that because the study detailed the impacts of a variety of specific IT factors on different aspects of quality of care, the data could help inform nursing home administrators about which features of an IT system are important to adopt to improve quality of care. This information could be very helpful to administrators and other leaders in making decisions about how to design and implement information systems.
The study appears in the Journal of Nursing Care Quality. A grant from the Department of Health and Human Services’ Agency for Healthcare Research and Quality supported this research.
Artificial intelligence looks set to transform nursing over the coming years.
If you think the digitisation of nursing is just about nurses filling out scores on a mobile device, it’s time to think again because artificial intelligence (AI) could be about to revolutionise the way nurses do their jobs. Recent digital developments include bottles which automatically issue reminders to drink, diapers that sound an alert when wet and sensor-equipped stoma pouches.
Heiko Mania, NursIT CEO, and a former nurse, believes AI will change the focus of nursing care: “Modern nursing expert software not only streamlines nursing documentation, it will automate it using AI, sensors and smart nursing aids. At the same time, professional nursing care will change from reactive to predictive, preventive nursing care.”
Mania said they had developed a nursing care expert system, CareIT Pro, which supports automation in nursing. He explained that smart algorithms and AI could reduce the need for information to be entered and could link content, so that further workflows and tasks could be automatically initiated at the right time. He added that the software automatically recognised patterns, evaluated the planned nursing goals and recommended necessary adaptations.
He said sensors, wearables and smart devices were also enabling increased automation: “Intelligent tools automatically deliver data on the patient to the nursing expert software and thus allow automated documentation. Alarms, nursing tasks and digital processes can be generated and started independently. Nursing staff not only receive digital to-do lists, but can also see the current status and quality of the nursing processes at all times and react to them at an early stage.
An intelligent drinking cup can automatically fill the drinking protocols and remind the patient to drink regularly or the stoma pouch sensor generates an automatic care task for changing the bag when it is almost full. We are currently developing an intelligent nursing mattress with a partner company that can detect not only the patient’s movement, breathing, position, pressure and sleep, but also incontinence.”
If the Internet of Things (IoT) is set to transform nursing, it is also starting to change the way nursing is taught. Widener University in Pennsylvania has introduced a range of simulation training from programming intravenous pumps and pumps for medication to updating electronic health records. It also runs disaster simulation training as Widener, in line with other nursing schools, has recognised the need to prepare nurses for such incidents in the wake of 9/11.
Nancy Laplante, Associate Professor of Nursing at Widener University, has recently published a paper arguing the case for introducing IoT in disaster training. Laplante believes this would highlight the application of these technologies in a meaningful way and enhance the experience for nursing students. She would like to teach the students to use mobile apps to track patients, triage them and track them to different hospitals, rather than using, for example, old-school paper-tagging of victims.
She said that downloading simple drawings, like Rich Pictures and Use Case Diagrams, which show all the participants in the disaster scene at a glance, can also improve understanding: “We were looking at what we call this rich picture for disaster scenarios and it was one way to help visualise all the interactions that would occur. What we wanted to do was to give students an understanding of how complex communication is in a mass casualty disaster scenario. It is not just nurses talking to patients; they are going to have to deal with fire fighters, police officers, bystanders and health providers that are off site.”
Laplante said students had to get to grips with new technology as it was a growth area. She said that nursing students had to understand, embrace and help develop new solutions as they could transform their practice. However, she pointed out that while IT was an important aspect of nursing, it could never take the place of nurses: “I don’t personally believe that nurses can ever be replaced because you always need that human touch. My hope and my feeling for technology is that it can help enhance our care.”
It is likely that technology will fundamentally change nursing over the coming years and, provided it is used correctly, it seems it really could improve the quality of care and lead to increased patient safety.
Duke University Pratt School of Engineering has established a new big data analytics center that will support global research to advance precision medicine.
Launched last month, the Sherry and John Woo Center for Big Data and Precision Health will receive more than $3 million in funding over the next three years from philanthropist and biotech industry executive John Woo. The Center will help Duke faculty and students develop innovative methods for turning big data into actionable clinical insights.
Investigators will have new opportunities to work with hospitals, government agencies, and biotech companies worldwide to advance data-driven health research.
“Big data, analytics, and machine learning are changing our world significantly, and nowhere will the change be more significant and meaningful than in healthcare,” said Ravi V. Bellamkonda, Vinik Dean of Engineering at Duke.
“Duke Engineering and Duke Health are collaboratively leading this change, and the Woo Center will help catalyze this further by coordinating new partnerships, expanding access to diverse, well-curated datasets and fueling transformative research ideas in this space.”
The center already has research efforts under way in China, where a team is developing a national network of health data parks to improve rural care delivery.
The new facility will also award annual pilot grants of up to $150,000 to Duke faculty so they can explore new ideas for collaborative projects.
In addition, the center will hold a yearly symposium to highlight significant findings to further build a global community of researchers. Leaders plan to sponsor global internships and exchanges for Duke students, as well as business plan and pitch competitions.
“Big data and precision medicine have the potential to vastly improve human health, and Duke has a special role to play with its unique combination of strengths in data science and machine learning, biomedical engineering and medicine—our faculty are world leaders in each of these areas,” said Larry Carin, Vice Provost for Research at Duke.
“Through new partnerships in China and around the world, we hope to address pressing medical issues in emerging markets and reduce disparities to improve global health.”
Duke University expects that the new Woo Center will add to its existing research efforts and will help foster the study of healthcare big data analytics
“Duke is already at the forefront of bringing big data and precision medicine into clinical practice,” said Xiling Shen, the Hawkins Family Associate Professor of Biomedical Engineering, and director of the new center.
“We’re excited about the opportunities this new center will open for our faculty and students to build productive new collaborations with clinicians and biotech companies to make an impact for patients.”
Other organizations have established similar facilities to improve care delivery.
In September 2018, the New Jersey Hospital Association launched a big data analytics center to identify and address gaps in care. Researchers plan to use predictive modeling and other analytics strategies to extract meaningful insights from big data.
“So many of the problems we see in healthcare today – racial and ethnic disparities, access to care barriers, variations in use of healthcare services, variables in access and funding of prevention and wellness – require a deeper dive intowhy,” said NJHA President and CEO Cathy Bennett.
“One of the ways we get closer to answering that question is to have solid data that shows us the root causes of these problems. We can then support design of solutions that address the foundation of the problem, rather than the symptoms.”
The University of California, Irvine (UCI), also recently launched an artificial intelligence center to help researchers develop deep learning tools and apply them to big data. The new center will allow researchers and faculty to collaborate and translate AI-based concepts into clinical tools that will improve patient health.
Additionally, Dell Medical School at the University of Texas at Austin has established a big data analytics center, called the Biomedical Data Science Hub. The facility will use big data analytics to enhance population health research, showing how both clinical and non-clinical factors affect health outcomes.
“To increase the pace of innovation in health, high-quality data needs to be ubiquitous and analysis much richer, and that’s what we’re trying to achieve with the data hub,” said Clay Johnston, MD, PhD, Dean of the Medical School.
“UT already has so much strength in this area, and now it’s about directing that toward the key questions in health including addressing health inequities in our community.”
More than 60 healthcare providers throughout the greater Rochester area are contributing patient EHRs to Rochester RHIO after receiving grants from a New York grant program, according to the Monroe County Post.
The grant from the Data Exchange Incentive Program (DEIP) is being used to offset setup costs for enrolling additional providers and patient health records into the regional health information exchange (HIE).
The DEIP was established in 2017 by the New York State Department of Health (DOH), with support from CMS. The grant program was launched to increase HIE adoption across the state. The New York eHealth Collaborative coordinates the programs and awards incentive payments on behalf of DOH.
“Hundreds of health care organizations were already sharing patient information, but as extensive as that data was, records were still not complete in many instances,” said Rochester RHIO President and CEO Jill Eisenstein. “With the help of DEIP, we’ve expanded our data sources to include groups such as skilled nursing facilities and diagnostic treatment centers.”
Rochester RHIO is one of eight qualified entities (QEs) part of the Statewide Health Information Network for New York (SHIN-NY). Organizations can add patient health data such as medications, lab test results, care plans, procedures, and other health information to offer providers a more comprehensive view of patient health spanning multiple care settings, facilities, and care teams through RHIO’s Contribute service.
Contribute allows providers to share patient data in the form of C-CDAs through provider EHR systems. Utilizing the Contribute service to add new patient health information to the exchange helps to enable better-informed clinical decision-making and improve care coordination.
Hospitals, healthcare organizations, private practices, and ambulatory care sites can gain access to this data with patient consent.
“By having a more complete digital record of care for each patient, health care providers can make more informed decisions,” Eisenstein said. “We’re looking forward to helping even more health care organizations connect, especially with the financial assistance from New York state.“
Increasing the number of providers contributing patient EHRs to the regional HIE will allow Rochester RHIO to provide a more complete view of patient health to each patient’s treating physician and care team. The grant supports the HIE’s mission to support high quality patient care across the community through the use of clinical data.
Grants from the DEIP can be used to build EHR interfaces that connect with QEs to increase the quantity and quality of data in SHIN-NY. The grant program was designed to help offset the costs of connecting to QEs for healthcare organizations by offering incentivizes to healthcare organizations that share a pre-defined set of data elements with other providers.
Two other New York-based HIEs part of SHIN-NY recently entered into a strategic partnership to boost HIE use among area providers.
HealthlinkNY and HealtheConnections partnered earlier this month after months of collaboration. HealthlinkNY first announced its decision to seek a strategic partner in 2017. Together, the two QEs cover 43 percent of providers across New York.
Prior to announcing its strategic partnership, HealthLinkNY stated its disapproval for another SHIN-NY QE that planned to expand its HIE services into HealthlinkNY’s territory.
Hixny planned to extend into nine additional counties over an 18 month period, stating health data exchange in those areas “historically lags.”
Several medical centers and clinics in regions covered by HealthlinkNY signed participation agreements with Hixny.
According to HealthlinkNY Executive Director Staci Romeo, Hixny’s expansion into territories covered by HealthlinkNY were the result of a “case of sour grapes after being passed over during our search for a strategic partner.”
HealthlinkNY covers 13 counties in the Hudson Valley, Catskills, and the Southern Tier.
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 Affairs, researchers 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.
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:
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.
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.
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.”