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The 2018 Healthcare Informatics Innovator Awards: First-Place Winning Team—UnityPoint Health

February 22, 2018
by Rajiv Leventhal
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Artificial intelligence across the continuum: preventing readmissions through patient-level heat maps
L to R: Ben Cleveland; Betsy McVay; Chris Hill, D.O.

Back in 2012, the Affordable Care Act (ACA) established the Hospital Readmissions Reduction Program (HRRP), which requires the Center for Medicare and Medicaid Services (CMS) to reduce payments to Inpatient Prospective Payment System (IPPS) hospitals with excess readmissions for certain medical conditions.

The HRRP was designed to make hospitals pay closer attention to what happens to their patients after they get discharged, but yearly data has shown that this has not been an easy task for patient care organizations. In 2016, the government penalized more than half of the nation’s hospitals—a total of 2,597—for having more patients than expected return within a month. Last year, the news didn’t get a whole lot better. According to Kaiser Health News, almost the same number of hospitals—this time 2,573—will be punished by Medicare for failing to lower their rehospitalization rates. On the financial side, according to KHN, Medicare will withhold $564 million in payments over the next year; the maximum reduction for any hospital is 3 percent.

To this point, hospitals and health systems have no shortage of motivation to keep patients from being readmitted unnecessarily. As officials at UnityPoint Health (UPH), an integrated health system with 29 hospitals, headquartered in Des Moines, Iowa, note, over the last few years, several of UPH’s hospitals did not meet their thresholds and were penalized to varying degrees.

Benjamin Cleveland, a data scientist at UPH, says, “Though readmissions often are influenced by factors largely outside of a healthcare system’s control, most systems conclude that discharge education, care coordination, and post-discharge intervention strategies offer the best chance at reducing their readmission rate.”

Cleveland adds that many readmission strategies are driven by three foundational components: which patients to focus on, what type of intervention should occur, and when the intervention should occur. “Which patients to focus on and when to target interventions lend themselves to the predictive modeling domain, while matching specific interventions with appropriate patients are best studied using controlled experiments,” he says. Rhiannon Harms, executive director of analytics at UPH, adds that analytics could answer the “who” and the “when” questions, which would then lead to care teams and providers being able to answer the “what” question around which type of intervention should occur.

Capturing the Comprehensive Picture

To this end, an enterprise-wide data analytics and artificial intelligence project—centered around preventing readmissions through patient-level “heat maps”—emerged at UPH, and was so innovative that it received first-place status in Healthcare Informatics’ 2018 Innovator Awards Program, Providers Division. Specifically, UPH Strategic Analytics sought to create a readmissions risk model that would be superior at identifying high-risk patients than other existing models—such as the commonly-used LACE [Length of stay, Acuity, Comorbidity and ED use] index and HOSPITAL [Hemoglobin, discharge from an Oncology service, Sodium level, Procedure performed, Index Type of admission (urgent vs. elective), number of Admissions in the last year, and Length of stay] score—as well as provide machine learning technologies to identify when along the 30-day spectrum a patient is most likely to be readmitted to guide care coordination efforts and ensure successful transitions.

These risk profiles would then be computed for every patient in the UPH system, and individualized to their own unique clinical and social scenarios to ensure a common “source of truth” for all parties involved across the care continuum, to coordinate the patient journey out of the hospital, UPH officials explain.

In the development of this project, Cleveland says that UPH partnered heavily with its local care teams to get feedback from everyone involved in the entire process—inclusive of primary care physicians, hospitalists, inpatient case managers, outpatient care coordinators, home health nurses, and more. “What do they see that affects readmissions? What do they think is tied to different patient risks that we should try to incorporate? Who do we talk to from an informatics perspective on how to best pull that data in the EHR [electronic health record]? It’s really a cross continuum, multi-disciplinary effort across our system,” he says.

The result of all of those discussions, organization-wide, says Cleveland, was that a core goal emerged: to capture a patient’s entire scenario comprehensively. “You want to capture the sociodemographic variables; all of our patients have different backgrounds,” he says. “And then you want to capture the severity of the condition they are being seen for today—inclusive of visits, medications, lab tests, and procedures that indicate severity. You also want to look at their past medical history to see what the [true] level of morbidity burden that they are carrying is, along with whatever the problem of the day is. And finally,” explains Cleveland, “You want to capture how they interact with their healthcare. Are there appointment no-shows, are they always late, are they going to their follow-ups? How many inpatient and ED visits have they had over the last few years? That’s how you get a comprehensive picture of the patient,” he attests.

In all, the team pulled in some 300,000 inpatient encounters and all of the associated variables with those encounters at each point in time, and then used machine learning algorithms to appropriately weight each of the variables. Overall, says Cleveland, morbidity burden and clinical severity within that visit were the factors that were most influential, followed by healthcare utilization and social demographics.

A Predictive Model Like No Other

Included in the feedback process and in the project’s development was Christopher Hill, D.O., medical director for clinical performance at UPH, who says that traditional models, such as LACE, might be based upon a DRG (diagnosis-related group) or a specific diagnosis. Bringing up the example of sepsis patients, traditionally, Hill says, “We may have looked at all sepsis patients as the same or maybe we looked at severe sepsis versus sepsis differently. But we did not look at the individual patient level where we take variables that statistically make a lot of sense, and we are seeing boots-on-the-ground activity around tying interventions to those variables and what we see on that heat map,” he says. “It’s quite different than traditional models because we are able to look at one patient’s individual risk which might be different than a cohort that we could have lumped [together] before,” he adds.

Speaking to the back-end technical side of the project, Cleveland says that continuously-learning ensemble algorithms are implemented and are supplemented with “a rich feature set to compute individualized predictions for all of our regions.” He adds, “The analytics engine operates in near real time, providing a robust risk profile across the care continuum to support not only the creation of a personalized post-discharge plan, but also simultaneously assessing the likelihood of plan success each day away from the hospital by tracking each patient’s appointment plan and alerting the care team to appointments that the patient is unlikely to show up for.”

Indeed, UPH officials note that one benefit of the heat map is that it can inform the ideal scheduling time of post-discharge follow-up appointments in order to address issues contributing to readmission before it is too late. And beyond that, Hill says that the heat map allows UPH leaders to continue to mature the prescriptiveness by which they can make decisions. For instance, there might be patients who value home care services or who might be better severed in a skilled nursing facility soon after discharge since they are at such high risk in the first week. “So we are continuing to mature that. That is the work we are actively working on—tying in all of the interventions, getting more perspective, and understanding how to put all that picture together, from the data to the appropriate interventions and [seeing] if it changed the outcome or their future chance of readmission,” says Hill.

Harris speaks further to the personalization aspect of the heat map, noting that prior to it being developed, there may have been a focus on getting all patients who were discharged into their primary care practice within seven days, as that would have been a measure that would have been followed by the care teams. “But now, a personalized view of the patient is [created], and maybe it’s realized that seven days is too late to intervene for that patient. We moved [away from] a one-sized-fits-all approach of follow-up within a week,” she says.

UPH leaders who were interviewed for this story also note how important it was to create a data-driven culture that would be accepted by everyone involved. Speaking to the change management piece involved, Betsy McVay, vice president and chief analytics officer, UPH, says that all of the care team groups were asked what was important to them and what would impact their workflow. “And we were purposeful about addressing the ones that were important to them. It continues to support our overall system work to being very data- and information-driven, and being embedded in helping to enable clinical outcome improvement,” says McVay.

Currently, all UPH regions are either using this model or are getting started with it and have the ability to do so, officials say. For the pilot site used to develop and adopt the readmission heat map, it has improved its risk-adjusted readmission index by 40 percent.

And moving forward, the project’s leaders have ambitious goals on how to continue evolving. From a technical standpoint, says Cleveland, “I would like to see how we can use analytics to inform the intervention that occurs with the patient. But I think our data and means for extracting data need to mature first.” He continues, “So for all of our readmitted patients, if I read the notes from the doctor and other care team members, are there indicators? And can I extract data in a meaningful way to actually find that signal that allows us to create the recommendations for those interventions?”

Meanwhile, Harris says she would like to continue to explore how they can use predictive modeling to create a roadmap for the patient ahead and how that supports the care team in helping patients navigate through their healthcare journeys. She says, “In this particular example, we have layered a length-of-stay model with the readmission risk model as well as our no-show model in the clinics to better understand who is unlikely to show up for their appointments. Some other predictive modeling might look at six months out for risk of admissions, so we have an opportunity to continue to layer these together to better provide a view of what the next six months or longer might look like for our patients.”

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Survey: Healthcare Orgs Ramping up Investment in AI, Confident about ROI

November 16, 2018
by Heather Landi, Associate Editor
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The majority of health care executives (91 percent) are confident they will see a return on investment (ROI) on artificial intelligence investments, although not immediately, and foresee the greatest impact of AI will be on improving health care, according to an OptumIQ survey.

Most (94 percent) health care leaders responded that their organizations continue to invest in and make progress in implementing AI, with 75 percent of healthcare organizations say they are implementing AI or have plans to execute an AI strategy, based on OptumIQ’s survey of 500 senior U.S. healthcare industry executives, primarily from hospitals clinics and health systems, life sciences organizations, health plans and employers. OptumIQ is the intelligence arm of data and analytics of Optum, an information and technology-enabled health services business that is part of UnitedHealth Group.

While many healthcare organizations have plans, progress is mixed across sectors. Of the 75 percent who are implementing AI or have plans to execute an AI strategy, 42 percent of those organizations have a strategy but have not yet implemented it. Employers are furthest along, with 22 percent reporting their AI implementations are at a late stage, with nearly full deployment.

The average AI implementation is estimated to cost $32.4 million over five years. The majority of respondents (65 percent) do not expect to see a ROI before four years with the average expected period being five years. However, employers (38 percent) and health plans (20 percent) expect ROI sooner, in three years or less, according to the survey.

The survey found that health care leaders universally agree the greatest impact of AI investment will be on improving health care. Thirty-six percent expect AI will improve the patient experience; 33 percent anticipate AI will decrease per-capita cost of care; and 31 percent believe AI will improve health outcomes.

Most health care leaders believe AI can make care more affordable and accessible. Ninety-four percent of respondents agree that AI technology is the most reliable path toward equitable, accessible and affordable health care.

AI will make care more precise and faster, according to respondents. The top two benefits respondents expect to see from incorporating AI into their organizations are more accurate diagnosis and increased efficiency.

The survey found that respondents are looking to AI to solve immediate data challenges – from routine tasks to truly understanding consumers’ health needs. Of those health organizations that are already investing in and implementing AI: 

  • 43 percent are automating business processes, such as administrative operations or customer service;
  • 36 percent are using AI to detect patterns in health care fraud, waste and abuse; and
  • 31 percent are using AI to monitor users with Internet of Things (IoT) devices, such as a wearable technology

With more organizations seeing the benefit of adopting an AI strategy, 92 percent agree that hiring candidates who have experience working with AI technology is a priority for their organization. To meet this need, nearly half (45 percent) of health care leaders estimate that more than 30 percent of new hires will be in positions requiring engagement with or implementation of AI in the next 12 months. However, health organizations seeking to hire experienced staff will likely face talent shortages.

“Artificial intelligence has the potential to transform health care by helping predict disease and putting the right insights into the hands of clinicians as they treat patients, which can reduce the total cost of care,” Eric Murphy, CEO of OptumInsight, said.

“Analytics isn't the end, it's the beginning – it's what you do with the insights to drive care improvement and reduce administrative waste,” Steve Griffiths, senior vice president and chief operating officer of Optum Enterprise Analytics, said. “For AI to successfully solve health care’s biggest challenges, organizations need to employ a unique combination of curated data, analytics and health care expertise... We are already seeing a race for AI talent in the industry that will grow as adoption continues to increase.”






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The 2019 Healthcare Informatics Innovator Awards Program is Open

November 15, 2018
by the Editors of Healthcare Informatics
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Providers and vendors can now submit their entries to the Healthcare Informatics Innovator Awards Program

The 2019 Healthcare Informatics Innovator Awards Program is now open for submissions. As always, it’s a great privilege and pleasure for us to sponsor this program.

And as many readers know, the concept of team-base recognition, which began with the 2009 edition of the program, has encompassed numerous sets of multiple winning teams that our publication has recognized for their achievements across a very broad range of areas.

As it always does, the Healthcare Informatics Innovator Awards Program recognizes leadership teams from patient care organizations—hospitals, physician groups, clinics, integrated health systems, payers, HIEs, ACOs, and other healthcare organizations—that have effectively deployed information technology in order to improve clinical, administrative, financial, or organizational performance.

The Innovators Program, as it has in the last few years, also recognizes vendor solution providers who are asked to describe their core products or services in five categories. We are asking vendors to submit their innovation in one of five critical health IT areas: Data Security; Value-Based Care; Revenue Cycle Management; Data Analytics; and Patient Engagement.

Indeed, again this year, the Innovator Awards program will again include two tracks for innovation recognition—one for healthcare provider organizations and one for technology solution providers.

The submission form link for both tracks is right here. The deadline for submissions is January 4, 2019.

What’s more, the winning teams will be featured in an upcoming issue of Healthcare Informatics, and winning vendor teams will be awarded free digital distribution of whitepapers to all HIT Summit Series attendees.

At Healthcare Informatics, we are honored to be able to showcase these kinds of case studies from both providers and vendors, which we believe embodies the spirit of innovation around adaptive change that will light the way for their colleagues from across the industry.

At a time of extraordinary change in healthcare, now is as great a time as ever to showcase your innovations. Please consider submitting an entry to our program, and good luck in your entry!

--The Editors of Healthcare Informatics


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New Blockchain Project Sets to Tackle Provider Credentialing

November 12, 2018
by Rajiv Leventhal, Managing Editor
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A group of five healthcare enterprises—National Government Services, Spectrum Health, WellCare Health Plans, Inc., Accenture, and The Hardenbergh Group—are linking up to participate in a distributed ledger program aimed at resolving administrative inefficiencies related to professional credentialing.

The project, Professional Credentials Exchange, is being developed by ProCredEx and Hashed Health, a blockchain innovation consortium. The exchange leverages “advanced data science, artificial intelligence, and blockchain technologies to greatly simplify the acquisition and verification of information related to professional credentialing and identity,” according to officials.

In an announcement, officials noted that credentialing healthcare professionals “is a universally problematic process for any industry member that delivers or pays for patient care.  The process often requires four to six months to complete and directly impedes the ability for a healthcare professional to deliver care and be reimbursed for their work.”

They added, “Hospitals alone forfeit an average of $7,500 in daily net revenues waiting for credentialing and payer enrollment processes to complete.  Further, nearly every organization required to perform this work does so independently—creating a significant administrative burden for practitioners.”

As such, the groups, via the exchange, will aim to address the time, cost, and complexity associated with these processes by facilitating the secure, trusted exchange of verified credentials information between exchange members.

Included in the collaboration are WellCare Health Plans, which serves about 5.5 million members, and Spectrum Health, a 12-hospital health system in western Michigan. National Government Services is a Medicare contractor for the Centers for Medicare & Medicaid Services (CMS), and processes more than 230 million Medicare claims annually.

"A fundamental component of developing the exchange lays in building a network of members that bring significant verified credential datasets to the marketplace," Anthony Begando, ProCredEx's co-founder and CEO, said in a statement.  "These are the leading participants in a growing group of collaborators who bring data and implementation capabilities to accelerate the deployment and scaling of the exchange."

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