Big data, advanced analytics, and digital offer many potential solutions to healthcare systems. But as powerful as these tools are, one size does not fit all. Illuminating patient experience and care pathways by performing data-to-insight and insight-to-action at scale requires reimagining processes through a holistic architecture that harnesses analytics, technology, and appropriate organizational design.
ACOs faced with many implementation challenges
The passage of the Affordable Care Act has changed the face of healthcare in the United States. Healthcare systems are gravitating toward the Accountable Care Organization (ACO) model to fill the upswing in demand with a payment and care delivery model that ties provider reimbursements to quality metrics. The dramatic increase in the covered population has added unprecedented complexity of clinical variation. The value-based care model of healthcare delivery can exist alongside the traditional fee-for-service model, but requires a step change in system, process, and analytics capabilities to capture value from both. The competing objectives of delivering higher quality care and reducing costs place immense pressure on existing systems and processes —a pressure which is only compounded by siloed data systems and the wide variety of information demanded by the ACO spectrum. And though ACOs are cost-effective in the long run, the costly data-reconciliation process means that many of them would need to operate for nearly two full years until their cash flow could be replenished through shared savings. In fact, studies have shown that an average ACO will risk about $4 million, plus additional feasibility and pre-application costs, before it can hope to see any fiscal payoff at all.
Many ACOs are preparing for future imperatives while still struggling to bring current-day operating performance to optimal levels. Workflows are being disrupted. Costs continue to rise for many. And improvement in quality of care is uneven.
Is technology the solution or the struggle?
One of the biggest developments in healthcare during the last quarter century has been the remarkable progress made in capturing patient, clinical, research, administrative, and cost data. ACOs are investing in their data and IT infrastructure to collect more data for improving clinical quality and driving greater efficiency, yet interoperability across systems and other providers remains a challenge. Various studies indicate that integrating data from multiple sources is not the only barrier; so is incorporating insight into workflows. Early advances in adopting electronic health records (EHR) and merging many systems hasn’t resulted in the next leap of progress, as data quality, access, and integration challenges prevent organizations from effectively leveraging data and analytics to derive value out of their investments. The prohibitive cost and poor ROI of healthcare information technology so far has become a key barrier to further implementations for organizations today.
Deployment of advanced digital technologies in patient-facing applications like tele-health, mobile health apps, wearables, and remote monitoring can improve access to care, boost efficiency, eliminate administrative bottlenecks, and increase the efficacy of treatment and preventive care. Yet organizations today are constrained by inadequate technology, analytics practices, and process flaws in key middle- and back-office functions.
The solution: A combination of lean principles, advanced technologies and domain know-how
Advanced analytics can help generate actionable insight from data being captured: identifying populations for targeted interventions, generating customized performance dashboards, and streamlining care protocols and provider management. Creating a master patient index capable of identifying and profiling individual patients from any data source is just the starting point for creating a “Patient 360” view1 with detailed and updated patient information being pulled from multiple data systems in an ACO environment. Establishing the right supply chain of data by pulling only the relevant, updated data from the right sources requires domain expertise and application of lean principles. Robust master data management and process-aware analytical engines provide a unified information exchange platform that integrates with care workflows and makes clinical data immediately available to practitioners and care teams, leading to better decisions around patient care and wellness (see figure 1).
Sophisticated analytical capabilities deliver little value if execution practices are not improved and underlying processes are not reimagined (see figure 2). This requires root-cause identification and lean practices to focus interventions where they will generate maximum impact. With the help of process and domain experts, redesigning key processes, such as centralized scheduling, patient access, and clinical trial recruitment end to end, can help deliver coordinated care.
Intelligent operations that continuously sense, act, and learn
Population health is substantially enhanced through forecasting, assessment, and the identification of key clinical care opportunities. Healthcare costs are optimized by capturing and tracking operating metrics like occupancy rates, physician load, and network management. Expenditures are consistently reduced by capturing and tracking financial metrics like productivity, billing, and operating cost analysis, and account receivables monitoring. And most important, lives are saved through patient-centric processes like patient/provider segmentation, disease management, and urgent-care alerts.
Transitioning to value-based population health models requires organizations to re-imagine conventional care and focus on the care continuum, rather than episodic care. These new business models demand new operating models to remain competitive, and continually improve quality of care metrics, that determine compensation. Effectively leveraging technology and analytics is across front, middle and back-office is critical to harness insights from touch points with patients, payers and providers networks to continuously improve care delivery and wellness management, ensure customer focus while still keeping costs practical.
Case Study: Case in point
A leading US healthcare delivery system transformed its operating model to successfully transition to value- based care. Specialized task allocation for case managers, revised operating procedures for care coordination, and targeted technology interventions like common access to patient health records, standardization of data and reporting, and instant alerts and notifications for high-risk patient profiles and incidents helped reduce re- admission rates and improve customer satisfaction while enhancing growth and reducing costs.
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