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The 2017 Healthcare Informatics Innovator Awards: Semifinalists

January 26, 2017
by the Editors of Healthcare Informatics
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We at Healthcare Informatics were once again elated by the outstanding quality of the submissions we received from innovating teams from across the U.S. In addition to the four winning teams this year, our editorial team also selected several runners-up. Below, please find descriptions of the initiatives of the nine teams whom we have awarded semifinalist status in this year’s program.

Association of Ontario Health Centers

A BI reporting tool supporting 85 organizations

In 2011, the Community Health Centers (CHCs) in Ontario, Canada had a vision to unify its data asset into a single enterprise data warehouse and associated business intelligence reporting tool (BIRT). Supporting 85 independent organizations, the vision was to unify accountability reporting to funders and create a robust self-service analytic environment. This was delivered within a security infrastructure that permits containment of sensitive clinical information but a shared BI environment where all organization users can share and collaborate the ad hoc reports among peers. The solution gives members a holistic view of operations by consolidating key data and presenting it in an integrated and easy-to-analyze manner. What makes this BI solution particularly unique is that it was built based on the needs of the members it serves, officials attest.

Bridging Access to Care

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Leveraging IT to integrate trauma informed care in everyday practice

Bridging Access to Care (BAC) aimed to facilitate trauma informed care by enhancing care delivery and augmenting plans of care to incorporate trauma specific objectives. The project plan included: developing a systematic approach using the electronic health record (EHR) system to inform and guide staff in delivering trauma sensitive activities; developing an electronic tool that could capture specific data and trend changes in consumer health outcomes; identifying validated tools that were simple to use, met industry standards and were recognized by payers; and creating a system for sharing information in real-time to facilitate access to treatment and retention in care. The project revealed that systematizing trauma informed care can improve consumer health outcomes. Post-implementation data revealed that 100 percent of clients were screened for trauma (compared to 0 prior to implementation); 37 percent of them were screened positive for trauma. And 92 percent of clients screened were referred to and are receiving seeking safety interventions, while 42 percent of clients that were screened were referred to and are receiving mental health services.

CHI Franciscan

Leveraging virtual care to transform healthcare delivery

CHI Franciscan Health, the Tacoma, Wash.-based health system that consists of eight hospitals and a large network of physician clinics, has a mandate to redesign its healthcare delivery network towards a value-based system serving the Pacific Northwest. The health system’s Care Transformation initiative is a massive project that is patient-focused in scope and has the dual benefit of added value to business and clinical outcomes. In the last three years, the Care Transformation team has grown from only four to a multi-disciplinary and multidimensional service line with more than 150 clinicians and IT professionals. The Care Transformation team consists of a number of providers, nurses, information technology specialists, analysts and others roles and responsibilities, all of which focus around providing care to patients where they are, and eliminating any geographic, time commitment and language barriers in accessing that care.

As a result of this team’s work, CHI Franciscan Health has successfully implemented an impressively broad range of programs and services leveraging technology, such as telemedicine. A virtual urgent care program has helped to save $1.5 million in healthcare costs and 10,511 hours of wait and travel time. A virtual intensive care unit program has helped to shorten ICU stays and increase ICU survivors. The ventilator bundle compliance has improved by 17 percentage points and is consistently staying at goal. A virtual diabetes education program was implemented to complement care services. Since its inception in 2012, 35 patients with diabetes have received high-touch interaction and education from diabetes educators and have demonstrated sustained decreases in their HgbA1C, have lost weight, stopped smoking and improved their activity levels. A regional telemetry monitoring program, the first at a U.S. health system, has enabled CHI Franciscan Health to proactively monitor patients’ heart rhythms and rates for over 2 million hours and engage 24 patients in the first six months from the launch before cardiac issues occur.

CHOC Children’s

Improving pediatric asthma management through population health

As part of Orange, Calif.-based Children’s Hospital of Orange County’s (CHOC Children’s) strategic plan for population health management, the CHOC Pediatric System of Care was formed to better manage pediatric lives. To support the management of the pediatric asthma population, hospital leadership implemented the Cerner Healthe Registries application in CHOC Children’s primary care clinics. Foundational to this project was the development and definition of asthma measures that, when completed on a routine basis, would improve the wellbeing of the patient by keeping their asthma well managed and out of emergency situations. Another important element of the initiative was developing tools to make the status measures available in real-time in the clinical workflow. To accomplish this, a team of providers developed ten defined measures that were built into the electronic health record (EHR). In the primary care physicians’ office, new workflows were rolled out to include a morning huddle with the entire care team where important information about the patients to be seen that day are reviewed including any outstanding measures for the asthma patients. Additionally, in order to track improvement, the project leaders built dashboards to provide clinicians with information on the completion of measures.

As a result of this initiative, emergency department visits for asthma related issues decreased by 18 percent and the hospital saw about $1 million in avoided emergency room costs. Additionally, children with an asthma action plan completed in the past year went from 15 percent to 27 percent. Children with an asthma control test completed in the past year went from 15 percent to 26 percent.                                                       

Geisinger Health System

Moving ahead to create a unified data architecture

The Danville, Pa.-based Geisinger Health System, already renowned across the U.S. healthcare system for its pioneering work in developing consensus- and evidence-based clinical pathways and its population health and care management innovations, has been moving ahead to facilitate further work to improve outcomes and curb costs, through its project to create a Unified Data Architecture (UDA), in order to integrate all of the organization’s analytic platforms. After concepting the initiative, Geisinger leaders decided to create a big data (Hadoop) platform, as the foundation for the UDA, and as the first phase of the project. Within a year, the team established a big-data platform, based on Hadoop and other open-source components. Within that time, Geisinger project team members developed code for a source ingestion pipeline to pull in source data, perform necessary transformations, and load data into various views; and having done so, pulled in all of the source data currently populating the data warehouse (EDW), plus additional sources not in the EDW.

A key element in all this is that Geisinger’s data and IT leaders “plan to deviate from industry standard and the common opinion that big data should augment the EDW, not replace it. We are on our way to proving that we can replace the EDW,” they state. “By running analytics from our Hadoop infrastructure, we have all of the benefits of distributed computing, plus the additional benefits of late binding and the ability to deal with non-discrete data, such as we find in clinic notes.”

Lakeland Health

Developing a hemorrhage risk assessment to improve maternal health

St. Joseph, Michigan-based Lakeland Health has applied electronic health record (EHR) technologies within its OB department at all three of its hospitals to support care providers in identifying warning symptoms in a timely manner to avoid costly complications. As the top two preventable causes of maternal morbidity and mortality are hemorrhage and preeclampsia, Lakeland Health set its sights on designing custom EHR tools to assist in the assessment and prevention of these conditions. Lakeland Health automated and implemented the following four innovative technology initiatives to manage obstetrical hemorrhage—a hemorrhage risk assessment; standardizing Pitocin administration during the third stage of labor; a quantitative blood loss calculator; and an OB emergency narrator hemorrhage risk assessment.

As a result of these projects, Lakeland Health has improved the amount of blood needed by new mothers. Before implementation in January 2015, the OB department used 37 units per 1,000 patients. In May 2016, the department used 24.8 units per 1,000 patients. Additionally, the project leaders have reported improvements in caring for obstetric patients with regard to preeclampsia. As of May 2016, 82 percent of obstetric patients had a completed hemorrhage risk assessment and with 98 percent of patients had blood pressure taken within 15 minutes of arrival and, of those with elevated blood pressure, care providers were notified 100 percent of the time upon the patient’s arrival. As part of this clinical informatics initiative, project leaders have found that process improvement reports and dashboards have been key elements for process improvement.

Texas Children’s Hospital

Working to improve patient alarm management

Looking for a way to reduce alarm noise and fatigue—one of the National Patient Safety Goals of the Joint Commission—leaders at Texas Children’s Hospital (Houston) formed an alarm management team, and reached out to Medical Informatics Corp. (MIC) to perform a baseline analysis to determine their current alarm situation and to come up with a plan. Using MIC’s dashboards, the alarm management team went beyond managing alarms, to improving care and processes. Over time, the scope of the project expanded, with the goal of making all alarms actionable to improve team communication, patient experience, and outcomes.

With participation from key stakeholder groups, including nurses, physicians, IT, biomedical engineering, and quality, and led by the hospital’s CMIO, Eric Williams, M.D., the team used baseline analysis and PDSA (Plan, Do, Study Act) methodology-based processes to reduce alarms and to modify alarm settings across one service area. As part of that process, nurse champions did daily audits of alarms at the unit and patient levels; and because the dashboards used included additional analytics, the nurse champions were able to make process improvements such as ensuring that one particular nurse didn’t end up being assigned the highest-alarming patients. The organization was able to meet NPSG goals in less than six months, and continues to see a reduction in alarm noise, with primary alarms dropping 9 percent over the course of 2016, and secondary alarms dropping 100 percent, and with specific alarms on individual patients dropping between 63 and 88 percent in frequency. The work continues, as PDSA-driven analyses are finding additional opportunities for improving alarm management.

University of California Davis Health System

Integrating patient-generated health data to improve health

The technology aim of UC Davis Health System’s diabetes and better blood pressure initiatives was to leverage patient-connected devices and integrate the patient-generated data points into the EHR, thus enabling clinicians to utilize coaching for behavior change when necessary. The recruited patients were provided an iHealth wireless blood pressure and on-boarded on how to “connect” their device to Apple HealthKit to the patient portal, which would feed into the clinicians view of the patients’ EHR. For the diabetes initiative, more than 1.4 million patient-generated health data points have been integrated into the EHR for clinical review and patient health management to-date. For the better blood pressure initiative, more than 2,700 data points have been made available to clinicians for review and patient health management. Patients participating in the initiatives have provided clinicians with improved visibility into patient health via ongoing data collection; clinicians now have the ability to collaborate with patients using real-time personalized data points.

UF Health Jacksonville

Addressing sepsis with smarter alerts

Clinician leaders at UF Health Jacksonville came together to address a combined set of problems that bedevil patient care organizations across the healthcare system: alert fatigue, and the need to manage the early signs of sepsis. Early detection of sepsis leads to early management which has been shown to decrease mortality; at the same time, as the clinical literature has noted, physicians and nurses are bombarded every day with a tsunami of alerts, which can lead to “alert fatigue,” and can ironically decrease awareness of impending dangers.

Leveraging a tool from Azrael, UF Health Jacksonville clinicians have created a program in which the standard alert interval is one alert per patient per 24-hour period. And if the program sees a worsening trend within a four-hour period along any of the parameters it is tracking, the tool will send another targeted alert. What’s more, the location-based logic embedded in the system allows each area within the hospital to customize the alert-firing threshold. The initiative has had excellent results, with the hospital’s sepsis mortality index (observed/expected ratio) falling from 0.83 in 2014 to 0.84 in 2015, and with its sepsis length-of-stay index decreasing from 1.25 in 2014 to 1.11 in 2015, while the actual sepsis mortality rate also continues to decrease.


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Have CIOs’ Top Priorities for 2018 Become a Reality?

December 12, 2018
by Rajiv Leventhal, Managing Editor
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In comparing healthcare CIOs’ priorities at the end of 2017 to this current moment, new analysis has found that core clinical IT goals have shifted from focusing on EHR (electronic health record) integration to data analytics.

In December 2017, hospitals CIOs said they planned to mostly focus on EHR integration and mobile adoption and physician buy-in, according to a survey then-conducted by Springfield, Va.-based Spok, a clinical communications solutions company, of College of Healthcare Information Management Executives (CHIME) member CIOs.

The survey from one year ago found that across hospitals, 40 percent of CIO respondents said deploying an enterprise analytics platform is a top priority in 2018. Seventy-one percent of respondents cited integrating with the EHR is a top priority, and 62 percent said physician adoption and buy-in for securing messaging was a top priority in the next 18 months. What’s more, 38 percent said optimizing EHR integration with other hospital systems with a key focus for 2018.

Spok researchers were curious whether their predictions became reality, so they analyzed several industry reports and asked a handful of CIOs to recap their experiences from 2018. The most up-to-date responses revealed that compared to last year when just 40 percent of CIOs said they were deploying an enterprise analytics platform in 2018, harnessing data analytics looks to be a huge priority in 2019: 100 percent of the CIOs reported this as top of mind.

Further comparisons on 2018 predictions to realities included:

  • 62 percent of CIOs predicted 2018 as the year of EHR integration; 75 percent reported they are now integrating patient monitoring data
  • 79 percent said they were selecting and deploying technology primarily for secure messaging; now, 90 percent of hospitals have adopted mobile technology and report that it’s helping improve patient safety and outcomes
  • 54 percent said the top secure messaging challenge was adoption/buy in; now, 51 percent said they now involve clinicians in mobile policy and adoption

What’s more, regarding future predictions, 87 percent of CIOs said they expect to increase spending on cybersecurity in 2019, and in three years from now, 60 percent of respondents expect data to be stored in a hybrid/private cloud.

CIOs also expressed concern regarding big tech companies such as Apple, Amazon and Google disrupting the healthcare market; 70 percent said they were somewhat concerned.

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How One Community Hospital is Leveraging AI to Bolster Its Care Pathways Process

December 6, 2018
by Heather Landi, Associate Editor
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Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.

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Sanders says he was intrigued by advances in machine learning tools and artificial intelligence (AI) platforms capable of applying advanced analytics to identify hidden patterns in data.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

“Those results are pretty amazing,” Sanders says. “It’s taking our data and showing us what we need to pursue. That’s powerful.”

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”

 


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At RSNA 2018, An Intense Focus on Artificial Intelligence

November 29, 2018
by Mark Hagland, Editor-in-Chief
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Artificial intelligence solutions—and discussions—were everywhere at RSNA 2018 this week

Artificial intelligence solutions—and certainly, the promotion of such solutions—were everywhere this year at the RSNA Conference, held this week at Chicago’s vast McCormick Place, where nearly 49,000 attendees attended clinical education sessions, viewed nearly 700 vendor exhibits. And AI and machine learning promotions, and discussions were everywhere.

Scanning the exhibit floor on Monday, Glenn Galloway, CIO of the Center for Diagnostic Imaging, an ambulatory imaging center in the Minneapolis suburb of St. Louis Park, Minn., noted that “There’s a lot of focus on AI this year. We’re still trying to figure out exactly what it is; I think a lot of people are doing the same, with AI.” In terms of whether what’s being pitched is authentic solutions, vaporware, or something in between, Galloway said, “I think it’s all that. I think there will be some solutions that live and survive. There are some interesting concepts of how to deliver it. We’ve been talking to a few folks. But the successful solutions are going to be very focused; not just AI for a lung, but for a lung and some very specific diagnoses, for example.” And what will be most useful? According to Galloway, “Two things: AI for the workflow and the quality. And there’ll be some interesting things for what it will do for the quality and the workflow.”

“Certainly, this is another year where machine learning is absolutely dominating the conversation,” said James Whitfill, M.D., CMO at Innovation Care Partners in Scottsdale, Ariz., on Monday. “In radiology, we continue to be aware of how the hype of machine learning is giving way to the reality; that it’s not a wholesale replacement of physicians. There have already been tremendous advances in, for example, interpreting chest x-rays; some of the work that Stanford’s done. They’ve got algorithms that can diagnose 15 different pathological findings. So there is true material advancement taking place.”

Meanwhile, Dr. Whitfill said, “At the same time, people are realizing that coming up with the algorithm is one piece, but that there are surprising complications. So you develop an algorithm on Siemens equipment, but when you to Fuji, the algorithm fails—it no longer reliably identifies pathology, because it turns out you have to train the algorithm not just on examples form just one manufacturer, but form lots of manufacturers. We continue to find that these algorithms are not as consistent as identifying yourself on Facebook, for example. It’s turning out that radiology is way more complex. We take images on lots of different machines. So huge strides are being made,” he said. “But it’s very clear that human and machine learning together will create the breakthroughs. We talk about physician burnout, and even physicians leaving. I think that machine learning offers a good chance of removing a lot of the drudgery in healthcare. If we can automate some processes, then it will free up our time for quality judgment, and also to spend time talking to patients, not just staring at the screen.”

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Looking at the hype cycle around AI

Of course, inevitably, there was talk around the talk of the hype cycle involving artificial intelligence. One of those engaging in that discussion was Paul Chang, M.D.., a practicing radiologist and medical director of enterprise imaging at the University of Chicago. Dr. Chang gave a presentation on Tuesday about AI. According a report by Michael Walter in Radiology Business, Dr. Chang said, “AI is not new or spooky. It’s been around for decades. So why the hype?” He described computer-aided detection (CAD) as a form of artificial intelligence, one that radiologists have been making use of for years.

Meanwhile, with regard to the new form of AI, and the inevitable hype cycle around emerging technologies, Dr. Chang said during his presentation that “When you’re going up the ride, you get excited. But then right at the top, before you are about to go down, you have that moment of clarity—‘What am I getting myself into?’—and that’s where we are now. We are upon that crest of magical hype and we are about to get the trench of disillusionment.” Still, he told his audience, “It is worth the rollercoaster of hype. But I’m here to tell you that it’s going to take longer than you think.”

So, which artificial intelligence-based solutions will end up going the distance? On a certain level, the answer to that question is simple, said Joe Marion, a principal in the Waukesha, Wis.-based Healthcare Integration Strategies LLC, and one of the imaging informatics industry’s most respected observers. “I think it’s going to be the value of the product,” said Marion, who has participated in 42 RSNA conferences; “and also the extent to which the vendors will make their products flexible in terms of being interfaced with others, so there’s this integration aspect, folding into vendor A, vendor B, vendor C, etc. So for a third party, the more they reach out and create relationships, the more successful they’ll be. A lot of it will come down to clinical value, though. Watson has had problems in that people have said, it’s great, but where’s the clinical value? So the ones that succeed will be the ones that find the most clinical value.”

Still, Marion noted, even the concept of AI, as applied to imaging informatics, remains an area with some areas lacking in clarity. “The reality, he said, “is that I think it means different things to different people. The difference between last year and this year is that some things are coming to fruition; it’s more real. And so some vendors are offering viable solutions. The message I’m hearing from vendors this year is, I have this platform, and if a third party wants to develop an application or I develop an application, or even an academic institution develops a solution, I can run it on my platform. They’re trying to become as vendor-agnostic as possible.”

Marion expressed surprise at the seemingly all-encompassing focus on artificial intelligence this year, given the steady march towards value-based healthcare-driven mandates. “Outside of one vendor, I’m not really seeing a whole lot of emphasis this year on value-based care; that’s disappointing,” Marion said. “I don’t know whether people don’t get it or not about value-based care, but the vendors are clearly more focused on AI right now.”

Might next year prove to be different? Yes, absolutely, especially given the coming mandates coming out of the Protecting Access to Medicare Act (PAMA), which will require referring providers to consult appropriate use criteria (AUC) prior to ordering advanced diagnostic imaging services—CT, MR, nuclear medicine and PET—for Medicare patients. The federal Centers for Medicare and Medicaid Services (CMS) will progress with a phased rollout of the CDS mandate, as the American College of Radiology (ACR) explains on its website, with voluntary reporting of the use of AUC taking place until December 2019, and mandatory reporting beginning in January 2020.

But for now, this certainly was the year of the artificial intelligence focus at the RSNA Conference. Only time will tell how that focus plays out in the imaging and imaging informatics vendor space within the coming 12 months, before RSNA 2019 kicks off one year from now, at the conference’s perennial location, McCormick Place.

 

 


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