HEALTHCARE INNOVATION SUMMIT RECAP Beth Israel Deaconess Care Organization’s

Analytics-Focused Leap Forward Scott Samways of the Beth Israel Deaconess Care Organization shared with attendees some of the breakthrough advances that he and his colleagues have been making in leveraging data analytics for population health management

By Mark Hagland D

uring the Northeast Healthcare Innovation Summit on Oct. 3 in downtown Boston and sponsored

by Healthcare Innovation, Scott Samways, director of data integration for Beth Israel Deaconess Care Organization (BIDCO), the accountable care organization (ACO) division of Beth Israel Leahy Health in Boston, presented the opening keynote, “Predictive Analytics for Data-Driven Care Management.” Samways shared with his audience highlights of the inno- vative work that he and his colleagues are doing around leveraging data to support leading-edge population health manage- ment and care management efforts. As explained on its website, BIDCO, which recently became a part of the what is now Beth Israel Leahy Health, is “a value-based physician and hospital network and ACO that partners with pro- viders to improve quality of care while effectively managing medical expenses. With its corporate office located in West- wood, Mass., BIDCO’s mission is to move healthcare forward by engaging provid- ers in their communities to achieve suc- cess in a value-based delivery system.” BIDCO, which earns $1.5 billion a year

in revenues, encompasses 200,000 cov- ered lives, 600 primary care physicians and 2,200 specialists, eight hospitals, and 35.6 million patient encounters docu- mented in more than 20 EHR (electronic health record) platforms. It is a participat- ing organization in the Medicare Shared Savings Program (MSSP), as well as with Medicaid and commercial health plans. As Samways noted, BIDCO services encompass a broad range of activity. Among the elements involved are popu- lation health (which in turn represents a hybrid model for community-based, high-risk complex care management and disease management; a home healthcare and SNF (skilled nursing facility) quality collaborative for care coordination; and ED utilization management for avoid- able admissions/visits); a performance improvement program that encompasses a Performance Improvement Facilitator for each practice; an EHR optimiza- tion program; facilitation for practice

redesign; the facilitation of understand- ing medical economics dashboards; ana- lytics and reporting (including quarterly financial performance reporting for risk units; desktop access to population health management tools; medical economics dashboards; and data surveillance); as well as contracting and network manage- ment services and enrollment services. What’s more, Samways said, he and his colleagues have spent at least six years so far integrating more than 160 individual EHRs across 20-plus EHR platforms, with more than 106 individual clinical data feeds and more than 30 claims feeds across more than 30 geographic locations, with more than 2 million patient records involved to date. “Interoperability is much more com- plicated than it looks,” Samways told his audience. “But we do the work: we connect the pipes, we get the data going. Then the reality sets in: is the data usable? In a lot of cases, it’s not. And so a huge amount of work around data normaliza- tion must take place. The EHR data we leverage—not everyone documents in the same way or at the same level, and that can require redesign of workflow in the practice, or may even require differ- ent configurations in the EHRs. And we don’t have an HIE standard in how we’re delivering that data,” Samways said. “We have structured data, unstruc-

tured data, and coded data,” Samways told the audience. Inevitably, he said, “We’ve had to meet our vendors where they are. They don’t always like to talk to each other, but we are connecting some of them through their back ends. Other vendors have really robust data feeds where they’ll send you the data on a nightly basis.” All the heteroge- neity among diverse EHR vendors remains problematic, he said. “We get into the dogs and cats things,” he said, referring to the tremendous diversity involved. “We’ve got a whole set of EHRs with all different capabilities. Sometimes we’re using flat files, some- times customized CCDAs. And we have a customized system that uses what we call CCDAs”—consolidated clinical

document architectures—but, he added, the organization’s custom-developed CCDAs remain cumbersome. Importantly, he noted, “Data map- ping, normalization, and validation are very resource-intensive and very chal- lenging for a team to maintain.” And the real-time performance in terms of successfully getting the right data to the right people at the right time remains a work in progress, he said. “How do we get usable data that the network trusts to the right people in a timely way? It’s one step at a time, and it’s a journey. We’ve been working six years now to identify and use data” in optimal ways. He also introduced the idea of the “dark data lake”—the vast amount of data in the overall system that has not yet been processed or used in any way. “It’s data we’re not yet mapping or using. We still want it, but a lot of work is involved to normalize, codify and map it.”

Moving forward, Samways and his colleagues are working to identify the most impactable patients, to match them with appropriate programs; to implement consistent, standardized, evidence-based workflows and care paths; to measure outcomes to identify best practices, to integrate platforms across multiple EHRs; and manage per- formance, both clinical and operational, across the health system. What’s more, Samways said, he and his colleagues are moving ahead to begin to leverage predictive analytics. “It’s not necessarily about identifying things happening right now,” he said. “Care programs can’t really impact inpatients or those in EDs. And it’s not about find- ing the exact outcome in the future. It is about identifying patterns and to infer trends and potential outcomes in the future, using existing information. It’s the marriage of art and science, for us. It’s taking clinicians’ knowledge and our rich data set, and putting them together. The goal is to improve conditions and outcomes, reduce cost and variation, and steer patients to the appropriate programs.” HI


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