Historically, data sharing across the healthcare continuum has been hindered by a host of challenges. These diverse issues range from data security concerns, technical standard issues, required infrastructure costs, and misaligned incentives surrounding participation.
Incremental progress has been made over the past decade through wide investment into electronic health record (EHR) systems. Unfortunately, this progress has been uneven. On the inpatient and outpatient side, core focus areas of EHR deployment have been in areas other than frictionless data exchange. Post-acute care has been even more challenged, having been largely excluded from the funds and technologies sparked by the HITECH Act. While capabilities continue to grow, there is much left to do in order to realize the benefits of closer collaboration, improved communication and optimized transitions promised by the ubiquitous exchange of data.
With emerging value-based payment models bringing renewed momentum and serving as a financial catalyst, care providers across the continuum are again pursuing rapid improvements in real-time data exchange. Under these new models, hospitals now bear direct risk for total episodic costs. They are explicitly placed in the driver’s seat, and despite having limited control over downstream services and often only marginal visibility into their patients’ journeys, they must navigate the road ahead.
To succeed in this new reality, hospitals and health systems are increasing taking a more aggressive stance in deploying the real-time data infrastructure essential to managing the clinical and financial outcomes of patients under their umbrella. Atop the technical requirements of such systems, obtaining buy-in necessitates a robust legal framework protecting both patient privacy and economic interests of network participants. Nevertheless, the promise of bridging care silos to manage care, report on key clinical and outcome measures, and proactively mitigate risk across the entire care continuum is essential for those health systems looking to thrive in a value-based world.
Understand Populations and Networks
Successfully improving clinical outcomes and lowering costs through real-time data sharing is based on a solid understanding of the patient populations of interest and the care delivery of providers involved. Bundles such as hip-and-knee replacement often require coordination across a network of provider partners, predicated on the needs and risk level of the patients. Other patients may be better suited for home discharge with remote monitoring and periodic telehealth interactions over the episode. Leveraging real-time data from standard Admission Discharge Transfer (ADT) feeds and risk prediction models, care managers can better balance the cost and efficacy of post-discharge care settings to the needs of specific segments of a population. The results are improved patient outcomes at more predictable, and frequently lower, cost.
Along with the populations to be managed, the strength and fit of partners must be considered early on. Consider what Baptist Health System in San Antonio reported from its participation in the initial CMS bundle program, Medicare Acute Care Episode (ACE) pilot. Of the roughly seventy skilled nursing facilities in the San Antonio area at the time, Baptist initially engaged at least two-thirds of them. Recognizing the need to narrow its network, Baptist followed a fact-based evaluation of its partners to determine their capabilities and readiness for participating in its network.
By concentrating spend in a carefully selected set of post-acute care providers, risk-bearing entities can reduce the administrative burdens of managing a broad network of providers, including vendor management and post-discharge patient tracking. Choosing higher quality PACs is a powerful hedge against unplanned readmissions, complications and rising cost.
Today, a common starting point is CMS data to help organizations understand and select the strongest providers in their region. Organizations may begin with the CMS Five-Star data sets, followed by more sophisticated claims data analytics to produce an in-depth understanding of underlying clinical and financial metrics.
Once real-time data sharing is in place, all participants can work from the same set of operational and quality metrics, eliminating the use of problematic, self-reported indicators. Ideally providers also have visibility across the full episode of care, at both the patient level and broader population level, to systematically identify and act on opportunities for improvement. Under fee-for-service reimbursement, great clinical outcomes were not always rewarded financially. With value-based care, partners have the data and incentives to continuously improve.
The Art of Focus
An early focus on populations and partners yields clarity around the scope, scale and potential impacts of data sharing. Likewise, starting with a clear focus on what data, if collected and refined into insights, will have the greatest impact on management of risk and improvement of care delivery help organizations is critical.
Too much data early on may create ‘noise’ in the process, outstripping the ability of organizations and individuals to absorb, understand and act. Rather than becoming mired in the countless use cases for real-time data integration with partners, use the focusing concept from Clayton Christiansen of “job to be done” to pinpoint areas of impact and then establish a progression of capabilities and the real-time data, analytics and predictions to support the process. The information overload on physicians that resulted from the deluge of well-intentioned EMR alerts should not be repeated on a network scale. When properly done, real-time data can enable network participants to proactively identify patients of rising risk and head off problems early rather than sifting through alerts, notifications, and reports. Near term impacts include shorter lengths of stay, fewer readmissions, more optimized care pathways, lower total costs, and better patient outcomes.
Alignment and Speed Yield Results
Harnessing the power of real-time data has the potential to dramatically improve care and reduce costs across the care continuum. A data sharing roadmap grounded in the clinical, operational, and financial results to be achieved and the desired process changes to be implemented sets the pace for the technical integration efforts, providing participants with the visibility to plan resources and investments. Careful alignment of incentives and rapid delivery of capabilities that drive measurable outcomes are critical for sustaining results.
About Neil Smiley, Founder and CEO, Loopback Analytics
Neil Smiley, a serial entrepreneur with a passion for transforming industries with data-driven solutions, founded Loopback Analytics in 2009 to deliver an advanced Software-as-a-Service platform healthcare providers can use to prevent costly readmissions. The Loopback Analytics team currently works with the largest pharmacy, hospitalist group, health system, payer and senior housing provider in the nation, providing proven intervention solutions that improve clinical outcomes and reduce the total cost of care. Prior to founding Loopback Analytics, Smiley launched Phytel, a population health solutions company that was successfully sold to a VC firm. Smiley began his career as an Accenture consultant and later as a partner with EY, working with Fortune 1000 clients. Smiley holds a computer science degree from Dartmouth College.