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Effort Focuses On Better Ways For Hospitals To Detect Drug Diversion

Posted on May 17, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Using a combination of machine learning technology and advanced analytics, a healthcare vendor has been working to find better ways to spot drug diversion in U.S. hospitals. The work done by the firm, Invistics, is funded by an NIH research grant.

The project has taken aim at a ripe target. According to a 2017 study by Porter Research, 96% of healthcare professionals who responded said that drug diversion happened often in their business. Also, sixty-five percent of respondents said that most diversion never gets detected. Clearly, there’s a hole you could drive a truck through in the drug dispensing process.

During the first stage of the research, Invistics worked with a pilot hospital to find opioid and drug theft across the entire facility. To get the job done, the vendor aggregated data from across the pilot hospital’s systems, including medical records, employee time clocks, wholesale purchasing, inventory and dispensing cabinets.

By leveraging data across several departments, Invistics got a much clearer view of potential problems than other efforts have in the past. The initiative was completely successful, with the technology picking out 100% of drug diversion happening within the project’s parameters, the company said. Since the completion of Phase I of the grant, Invistics has rolled out the solution at several other hospitals.

When it comes to avoiding opioid abuse, far morer attention has been focused on patterns of opioid prescribing, with the assumption that the opioid addiction epidemic can be stemmed at the source. For example, we recently covered a study looking at post hospital-discharge opioid use which centered on predicting which patients would be on chronic opioid therapy after discharge and planning for that discharge appropriately.

There’s no question that such research has a place in the battle against opioid misuse and abuse. After all, it seems likely that at least some needless addictive patterns stem from physician prescribing habits. It also makes sense that states are revising their guidelines for opioid prescribing, though to my knowledge these changes are being based more on ideology than rigorous research.

On the other hand, drug diversion creates a pipeline between drug supplies and drug abusers which must be addressed directly if the opioid abuse war is to be won. I for one was interested to learn about a solution that addresses this piece of the puzzle.

Improving Data Outcomes: Just What The Doctor Ordered

Posted on May 8, 2018 I Written By

The following is a guest blog post by Dave Corbin, CEO of HULFT.

Health care has a data problem. Vast quantities are generated but inefficiencies around sharing, retrieval, and integration have acute repercussions in an environment of squeezed budgets and growing patient demands.

The sensitive nature of much of the data being processed is a core issue. Confidential patient information has traditionally encouraged a ‘closed door’ approach to data management and an unease over hyper-accessibility to this information.

Compounding the challenge is the sheer scale and scope of the typical health care environment and myriad of departmental layers. The mix of new and legacy IT systems used for everything from billing records to patient tracking often means deep silos and poor data connections, the accumulative effect of which undermines decision-making. As delays become commonplace, this ongoing battle to coordinate disparate information manifests itself in many different ways in a busy hospital.

Optimizing bed occupancies – a data issue?

One example involves managing bed occupancy, a complex task which needs multiple players to be in the loop when it comes to the latest on a patient’s admission or discharge status. Anecdotal evidence points to a process often informed manually via feedback with competing information. Nurses at the end of their shift may report that a patient is about to be discharged, unaware that a doctor has since requested more tests to be carried out for that patient. As everyone is left waiting for the results from the laboratory, the planned changeover of beds is delayed with many knock-on effects, increasing congestion and costs and frustrating staff and patients in equal measure.

How data is managed becomes a critical factor in tackling the variations that creep into critical processes and resource utilization. In the example above, harnessing predictive modelling and data mining to forecast the number of patient discharges so that the number of beds available for the coming weeks can be estimated more accurately will no doubt become an increasingly mainstream option for the sector.

Predictive analytics is great and all, but first….

Before any of this can happen, health care organizations need a solid foundation of accessible and visible data which is centralized, intuitive, and easy to manage.

Providing a holistic approach to data transfer and integration, data logistics can help deliver security, compliance, and seamless connectivity speeding up the processing of large volumes of sensitive material such as electronic health records – the kind of data that simply cannot be lost. These can ensure the reliable and secure exchange of intelligence with outside health care vendors and partners.

For data outcomes, we’re calling for a new breed of data logistics that’s intuitive and easy to use. Monitoring interfaces which enable anyone with permission to access the network to see what integrations and transfers are running in real time with no requirement for programming or coding are the kind of intervention which opens the data management to a far wider section of an organization.

Collecting data across a network of multiple transfer and integration activities and putting it in a place where people can use, manage and manipulate becomes central to breaking down the barriers that have long compromised efficiencies in the health care sector.

HULFT works with health care organizations of all sizes to establish a strong back-end data infrastructure that make front-end advances possible. Learn how one medical technology pioneer used HULFT to drive operational efficiencies and improve quality assurance in this case study.

Dave Corbin is CEO of HULFT, a comprehensive data logistics platform that allows IT to find, secure, transform and move information at scale. HULFT is a proud sponsor of Health IT Expo, a practical innovation conference organized by Healthcare Scene.  Find out more at hulftinc.com

Henry Ford Rolling Out Analytics In Neuro ICU

Posted on April 25, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Not long ago, the chair of neurology at the sprawling Henry Ford Hospital decided it was time to bring his idea to life. Dr. Stephan Mayer, who had previously created a data analytics system at New York Columbia-Presbyterian Medical Center, felt he could bring what he learned to Henry Ford Hospital — and that it could save lives.

According to a story in Crain’s Detroit Business, Mayer was convinced that if the hospital analyzed data generated by patient monitors, it could reduce mortality and complications by predicting negative patient events.

“This is all about lost opportunity and making the most of the data we have,” Mayer told Crain’s. “There is nothing unique about the data we have. We have EMRs connected to pharmacy, radiology, billing, this and that, but there is a doughnut hole. The empty spot is the ICU, where the sickest  of the people are.”

Acting on that belief, Mayer put together an initiative bringing such tools to the health system’s neuro ICU.

After searching for a partner that could make this happen, Mayer settled on Medical Informatics Corp.’s FDA-cleared clinical intelligence platform, Sickbay, which monitors real-time vital signs issued by any connected device. The Sickbay product also comes with related apps such as Multimon, which allows clinicians to view multiple patients remotely across units, the hospital or multiple facilities.

Once deployed, Sickbay collects patient monitor data, stores and organizes it in a manner making it easier for clinicians to predict future patient events. For example, it can produce data on patient alarms that fall within specified critical ranges. This allows clinicians to see and act on patterns more quickly, Mayer said.

Working with Henry Ford’s IT Department, Mayer is rolling out Sickbay. Starting in June, Henry Ford will launch Sickbay and begin storing patient data. Over the next six months, the neuro ICU should collect data on 600 patients. Hopefully, this data will offer clinicians the insight and context they need to help patients.

If Mayer gets the results he’s hoping for, this could be just the first in a series of rollouts, potentially across the 22 ICUs operating across the five-hospital system. “Our organization is eager to push boundaries,” he told the magazine. “What we are doing, if it works as planned…it will change the way we round in the ICU.”

This sounds great, but Mayer is still lucky he’s at Henry Ford rather than other less-entrepreneurial organizations. The health system has worked to promote technology innovation for many years. Its efforts include an innovations program rewarding employees for standout inventions in areas like clinical applications for wearable technology.

Translating from Research to Bedside

Posted on April 2, 2018 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I’m increasingly interested in how we bridge the gap between research and practice in healthcare. No doubt my increased interest comes from the need to prove the value of data and technology in healthcare.

Remember that when we first started introducing EHR software into healthcare, the main goals were around billing and possibly efficiency. The former one has been a success in many aspects and the former has been a pretty big failure. However, the focus was never initially on how to improve care and the focus on billing has actually had a negative impact on care in ways that most people didn’t expect.

Now we’re seeing healthcare organizations trying to shift EHR models so that they do work to improve care. This has proven to be a challenge and it’s no doubt why many healthcare organizations are going beyond the EHR to make population health happen.

The other problem with moving into the clinical improvement space is that the bar is much higher. No one minds too much if you take risks in billing. That’s why most AI (Artificial Intelligence) is starting there as well. However, when you start dealing with the clinical aspects of healthcare, you have to take a much different approach and requires proper research of proposed ideas and methods.

Therein lies the challenge for much of the healthcare IT innovation. There’s a large gap between researchers and the bedside. This was highlighted really well by a researcher who described the challenge of translating research into medicine:

Speaker 3: The current models are not translational. We need more innovation and check out my cool data that does not address the topic.

The moderator was clearly the speaker’s past mentor as extra time was spent introducing this investigator’s novel interpretation of the topic. The introduction slide simply said NO in bold letters and the speaker launched into a TedX style talk on how these models are not translational and it is a waste of time for the Department of Defense or NIH to fund multi-team consortium to develop new relevant models. Remember, it was a panel discussion. This speaker left the panel and walked into the crowd spouting off about how translational research as it is defined would not prove useful and innovation was required to develop new therapies. In addition, replicative studies or lack of replication was moot because one can’t trust how other scientists conduct their science. As an example of innovation, studies demonstrating the effective integration of neuronal progenitor cells into the brain of a mouse model of epilepsy were shared. These studies were not done in a traumatic brain injury model, but a different model entirely. Innovative and published in a well-regarded journal, yes; translational, not likely and only time and additional studies will determine; relevant to the topic, no. Supporters of this young investigator probably called this display brave. There were no answers to be found here, only self-promotion. The presentation was not designed for discussion amongst peers, but was strategically delivered to help the investigator’s career trajectory. The song and dance number did not reflect a dedication to developing new therapies for people following a traumatic brain injury.

A successful Investigator’s Workshop speaker will address the topic using scientific data, but most importantly capture a story for the audience. Ideally, bullet points from learned experience or on which the speaker would like feedback will be shared and will foster discussion amongst the moderator, panelists, and audience members. It is an opportunity for the scientist to improve their approach as well as inform the audience.

This was an important insight to remember as we consider how to incorporate research into healthcare IT. The motivations of researchers are often not aligned with translating their research into practice. Researcher’s focus is often on career promotion, grant dollars, and publications. That’s a real disconnect between what most health IT vendors and healthcare organizations want to achieve.

Health Leaders Go Beyond EHRs To Tackle Value-Based Care

Posted on March 30, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

In the broadest sense, EHRs were built to manage patient populations — but largely one patient at a time. As a result, it’s little wonder that they aren’t offering much support for value-based care as is, as a recent report from Sage Growth Partners suggests.

Sage spoke with 100 healthcare executives to find out what they saw as their value-based care capabilities and obstacles. Participants included leaders from a wide range of entities, including an ACO, several large physician practices and a midsize integrated delivery network.

The overall sense Sage seems to have gotten from its research was that while value-based care contracts are beginning to pay off, health execs are finding it difficult support these contacts using the EHRs they have in place. While their EHRs can produce quality reports, most don’t offer data aggregation and analytics, risk stratification, care coordination or tools to foster patient and clinician engagement, the report notes.

To get the capabilities they need for value-based contracting, health organizations are layering population health management solutions on top of their EHRs. Though these additional PHM tools may not be fully mature, health executives told Sage that there already seeing a return on such investments.

This is not necessarily because these organizations aren’t comfortable with their existing EHR. The Sage study found that 65% of respondents were somewhat or highly unlikely to replace their EHR in the next three years.

However, roughly half of the 70% of providers who had EHRs for at least three years also have third-party PHM tools in place as well. Also, 64% of providers said that EHRs haven’t delivered many important value-based contracting tools.

Meanwhile, 60% to 75% of respondents are seeking value-based care solutions outside their EHR platform. And they are liking the results. Forty-six percent of the roughly three-quarters of respondents who were seeing ROI with value-based care felt that their third-party population PHM solution was essential to their success.

Despite their concerns, healthcare organizations may not feel impelled to invest in value-based care tools immediately. Right now, just 5% of respondents said that value-based care accounted for over 50% of their revenues, while 62% said that such contracts represented just 0 to 10% of their revenues. Arguably, while the growth in value-based contracting is continuing apace, it may not be at a tipping point just yet.

Still, traditional EHR vendors may need to do a better job of supporting value-based contracting (not that they’re not trying). The situation may change, but in the near term, health executives are going elsewhere when they look at building their value-based contracting capabilities. It’s hard to predict how this will turn out, but if I were an enterprise EHR vendor, I’d take competition with population health management specialist vendors very seriously.

Study Offers EHR-Based Approach To Predicting Post-Hospital Opioid Use

Posted on March 27, 2018 I Written By

Sunny is a serial entrepreneur on a mission to improve quality of care through data science. Sunny’s last venture docBeat, a healthcare care coordination platform, was successfully acquired by Vocera communications. Sunny has an impressive track record of Strategy, Business Development, Innovation and Execution in the Healthcare, Casino Entertainment, Retail and Gaming verticals. Sunny is the Co-Chair for the Las Vegas Chapter of Akshaya Patra foundation (www.foodforeducation.org) since 2010.

With opioid abuse a raging epidemic in the United States, hospitals are looking for effective ways to track and manage opioid treatment effectively. In an effort to move in this direction, a group of researchers has developed a model which predicts the likelihood of future chronic opioid use based on hospital EHR data.

The study, which appears in the Journal of General Internal Medicine, notes that while opioids are frequently prescribed in hospitals, there has been little research on predicting which patients will progress to chronic opioid therapy (COT) after they are discharged. (The researchers defined COT as when patients were given a 90-day supply of opioids with less than a 30-day gap in supply over a 180-day period or receipt of greater than 10 opioid prescriptions during the past year.)

To address this problem, researchers set out to create a statistical model which could predict which hospitalized patients would end up on COT who had not been on COT previously. Their approach involved doing a retrospective analysis of EHR data from 2008 to 2014 drawn from records of patients hospitalized in an urban safety-net hospital.

The researchers analyzed a wide array of variables in their analysis, including medical and mental health diagnoses, substance and tobacco use, chronic or acute pain, surgery during hospitalization, having received opioid or non-opioid analgesics or benzodiazepines during the past year, leaving the hospital with opioid prescriptions and milligrams of morphine equivalents prescribed during their hospital stay.

After conducting the analysis, researchers found that they could predict COT in 79% of patients, as well as predicting when patients weren’t on COT 78% of the time.

Being able to predict which patients will end up on COT after discharge could prove to be a very effective tool. As the authors note, using EHR data to create such a predictive model could offer many benefits, particularly the ability to identify patients at high risk for future chronic opioid use.

As the study notes, if clinicians have this information, they can offer early patient education on pain management strategies and where possible, wean them off of opioids before discharging them. They’ll also be more likely to consider incorporating alternative pain therapies into their discharge planning.

While this data is exciting and provides great opportunities, we need to be careful how we use this information. Done incorrectly it could cause the 21% who are misidentified as at risk for COT to end up needing COT. It’s always important to remember that identifying those at risk is only the first challenge. The second challenge is what do you do with that data to help those at risk while not damaging those who are misidentified as at risk.

One issue the study doesn’t address is whether data on social determinants of health could improve their predictions. Incorporating both SDOH and patient-generated data might lend further insight into their post-discharge living conditions and solidify discharge planning. However, it’s evident that this model offers a useful approach on its own.

Hospitals Centralizing Telemedicine, But EMR Integration Is Still Tough

Posted on March 26, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Over the past few years, large healthcare providers have begun to offer their patients telemedicine options. In the past, they offered these services on an ad-hoc basis, but that seems to be changing. A new survey suggests that hospitals and health systems have begun to manage this telemedicine service lines to a central office rather than letting individual departments decide how to deliver virtual care.

The survey, which was conducted by REACH Health, polled more than 400 healthcare executives, physicians and nurses as well as other healthcare professionals. REACH, which offers enterprise telemedicine systems, has been conducting research on the telemedicine business for several years.

Forty-eight percent of respondents to the REACH Health 2018 Telemedicine Industry Benchmark Survey reported that they coordinated telemedicine services on enterprise-level, up from 39% last year. Meanwhile, 26% said that individual departments handled their own telemedicine services, down from 36% in 2017.

The providers that are taking an enterprise approach seem to have a good reason for doing so. When it analyzed the survey data, REACH concluded that organizations offering telemedicine at the enterprise level were 30% more likely to be highly successful. (Not that the company would draw any other conclusion, of course, but it does seem logical that coordinating telehealth would be more efficient.)

The survey also found that telemedicine programs provided by both behavioral health organizations and clinics have expanded rapidly over the last few years. Back in 2015, REACH found that many behavioral health providers and clinics were at the planning stages or new to delivering telemedicine, but according to the 2018 results, many now have active telemedicine programs in place, with clinic services expanding 37% and behavioral health 40%.

While healthcare organizations may be managing telemedicine centrally, their EMRs don’t seem adequate to the job. First, most survey respondents noted that the telemedicine platform wasn’t integrated with the EMR. Meanwhile, nearly half said they were documenting patient visits in the EMR after remote consultations had ended. In addition, more than one-third of respondents said that EMR doesn’t allow them to analyze telemedicine-specific metrics adequately.

Whether REACH’s solution solves the problem or not, I’m pretty sure they’re right that integrating telemedicine services data with an EMR remains difficult.

In fact, it seems obvious to me that while hospitals are still tweaking their programs for maximum impact, and getting paid for such services is still an issue, telemedicine won’t become a completely mature service line until collecting related data and integrating it with off-line patient care information is easy and efficient.

 

Mayo Clinic Creating Souped-Up Extension Of MyChart

Posted on March 19, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

As you probably know, MyChart is Epic’s patient portal. As portals go, it’s serviceable, but it’s a pretty basic tool. I’ve used it, and I’ve been underwhelmed by what its standard offering can do.

Apparently, though, it has more potential than I thought. Mayo Clinic is working with Epic to offer a souped-up version of MyChart that offers a wide range of additional services to patients.

The new version integrates Epic’s MyChart Virtual Care – a telemedicine tool – with the standard MyChart mobile app and portal. In doing so, it’s following the steps of many other health systems, including Henry Ford Health System, Allegheny Health Network and Lakeland Health.

However, Mayo is going well beyond telemedicine. In addition to offering access to standard data such as test results, it’s going to use MyChart to deliver care plans and patient-facing content. The care plans will integrate physician-vetted health information and patient education content.

The care plans, which also bring Mayo care teams into the mix, provide step-by-step directions and support. This support includes decision guidance which can include previsit, midtreatment and post-visit planning.

The app can also send care notifications and based on data provided by patients and connected devices, adapt the care plan dynamically. The care plan engine includes special content for conditions like asthma, type II diabetes chronic obstructive heart failure, orthopedic surgery and hip/knee joint replacement.

Not surprisingly, Mayo seems to be targeting high-risk patients in the hopes that the new tools can help them improve their chronic disease self-management. As with many other standard interventions related to population health, the idea here is to catch patients with small problems before the problems blossom into issues requiring emergency department visit or hospitalization.

This whole thing looks pretty neat. I do have a few questions, though. How does the care team work with the MyChart interface, and how does that affect its workflow? What type of data, specifically, triggers changes in the care plan, and does the data also include historical information from Mayo’s EMR? Does Mayo use AI technology to support care plan adaptions? Does the portal allow clinicians to track a patient’s progress, or is Mayo assuming that if patients get high high-quality educational materials and personalized care plan that the results will just come?

Regardless, it’s good to see a health system taking a more aggressive approach than simply presenting patient health data via a portal and hoping that this information will motivate the patient to better manage their health. This seems like a much more sophisticated option.

Tri-City Medical Center: Achieving a Middleware First

Posted on March 2, 2018 I Written By

The following is a guest blog post by Adam Klass, Chief Technology Officer, VigiLanz.

In the age of value-based care, it’s all about performance as hospitals continually face increased financial pressure to meet a number of different criteria related to decreasing length of stay, hospital-acquired infection rates and hospital readmissions. Today’s hospital organization must improve healthcare analytics and core measures, avoid penalties, and secure reimbursement, so it can continue to grow and thrive. This shift means hospitals must now consider cost avoidance instead of expecting direct reimbursement for patient care.

The challenge then becomes how to support and enable next-generation healthcare providers by delivering real-time results from disparate platforms and technology into any clinical workflow. It’s no surprise, then, that 62 percent of hospital CIOs identify interoperability as a top priority and 80 percent of accountable care organizations also cite integrating data as a top challenge for their IT departments.

To accomplish this goal, medical facilities like Tri-City Medical Center, a 388-bed full service, acute care hospital in Oceanside, California, require a services-oriented architecture and open application programming interface (API) capability that enables efficient aggregation, interaction and exchange of disparate data throughout the healthcare enterprise and across any of its software technologies, including EMRs and third-party single-point-solution vendors.

APIs Versus HL7

APIs fit the bill by allowing access to all of the data a digital health application and a health system would need, in real-time. Clinicians and administrators can now rapidly integrate new clinical and business information for better decision-making and, most importantly, for improved patient care with new interoperability services.

Tri-City Medical Center, which also operates a primary care clinic and employs more than 700 physicians practicing in 60 specialties, is the first VigiLanz customer site to utilize our middleware API solution, VigiLanz Connect, to convert health data from its EMR into uniform, actionable intelligence in the VigiLanz Platform. The hospital organization’s use of this solution turns its closed EMR systems into open platforms through robust services that do not rely on HL7 interfaces. Instead, our platform handles connectivity and normalizes data structures across major EMR platforms, like Cerner’s, which Tri-City Medical Center uses, to quickly unlock the data. Benefits include reduced integration time from months to days, elegant workflows, decreased maintenance costs and minimized risk.

“An API is definitely the way to go,” explained Mark Albright, Vice President of Technology, Tri-City Medical Center. “Anytime we have a choice between an interface and an API, we always go with APIs. It’s just so much easier to install and get up and running.”

“Not only are APIs easy to use but they are a no-brainer when it comes to rapid and successful implementation,” continued Albright. “Using VigiLanz’s middleware API helped us maximize the platform in a different, modern way. Not only is it a simpler effort than using a solution like HL7 but it’s also stable and steady so it’s easy to maintain, despite the significant amount of data being pulled.”

Taking EMR Systems to the Next Level

Clinical intelligence and interoperability services complement today’s EMR systems which, on their own, may be insufficient to deliver agile, real-time intelligence services. In contrast, a middleware API can interoperate with EMR systems and is built with innovative abstract data architectures that help hospitals like Tri-City Medical Center improve patient care and operational performance.

In contrasting his organization’s middleware API experience with what would have traditionally been an HL7 integration, Albright noted, “Our hospital charged a non-programmer, non-developer, non-HL7 person with spearheading this project, something that could have not happened in an HL7 world. She would have never been able to master that.”

That “she” is Melody Peterson, a senior systems analyst, who stepped into the project post-decision, after Tri-City’s pharmacy, infection control and clinical surveillance departments had already made the decision to purchase the middleware API, separate from the organization’s IT department.

“I was tasked with making this middleware API work, without having been part of the research or purchase decision,” explained Peterson. “Because VigiLanz supports the clinical and business sides of our hospital, though, it was easy to implement this ‘plug-and-play’ integration solution, in a way that applied to all areas critical to optimizing care – from risk scoring to antimicrobial stewardship.”

A middleware architecture is often the best technological solution for addressing the problem of EHR interoperability because it facilitates the transparent, yet secure, access of patient health data, directly from the various databases where it is stored. No longer does a hospital organization like Tri-City Medical Center have to do all of the development itself, but instead can rely on off-the-shelf applications to solve problems. Middleware brings an application-agnostic approach to connecting EMRs to one another while allowing for specific development to enhance the significant investment by hospitals, health systems and physicians.

Yale New Haven Hospital Partners With Epic On Centralized Operations Center

Posted on February 5, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Info, info, all around, and not a place to manage it all. That’s the dilemma faced by most hospitals as they work to leverage the massive data stores they’re accumulating in their health IT systems.

Yale New Haven Hospital’s solution to the problem is to create a centralized operations center which connects the right people to real-time data analytics. Its Capacity Command Center (nifty alliteration, folks!) was created by YNHH, Epic and the YNHH Clinical Redesign Initiative.

The Command Center project comes five years into YNHH’s long-term High Reliability project, which is designed to prepare the institution for future challenges. These efforts are focused not only on care quality and patient safety but also managing what YNHH says are the highest patient volumes in Connecticut. Its statement also notes that with transfers from other hospitals increasing, the hospital is seeing a growth in patient acuity, which is obviously another challenge it must address.

The Capacity Command Center’s functions are fairly straightforward, though they have to have been a beast to develop.

On the one hand, the Center offers technology which sorts through the flood of operational data generated by and stored in its Epic system, generating dashboards which change in real time and drive process changes. These dashboards present real-time metrics such as bed capacity, delays for procedures and tests and ambulatory utilization, which are made available on Center screens as well as within Epic.

In addition, YNHH has brought representatives from all of the relevant operational areas into a single physical location, including bed management, the Emergency Department, nursing staffing, environmental services and patient transport. Not only is this a good approach overall, it’s particularly helpful when patient admissions levels climb precipitously, the hospital notes.

This model is already having a positive impact on the care process, according to YNHH’s statement. For example, it notes, infection prevention staffers can now identify all patients with Foley catheters and review their charts. With this knowledge in hand, these staffers can discuss whether the patient is ready to have the catheter removed and avoid related urinary tract infections associated with prolonged use.

I don’t know about you, but I was excited to read about this initiative. It sounds like YNHH is doing exactly what it should do to get more out of patient data. For example, I was glad to read that the dashboard offered real-time analytics options rather than one-off projections from old data. Bringing key operational players together in one place makes great sense as well.

Of course, not all hospitals will have the resources to pull something off something like this. YNHH is a 1,541-bed giant which had the cash to take on a command center project. Few community hospitals would have the staff or money to make such a thing happen. Still, it’s good to see somebody at the cutting edge.