The 7 Stages of Value-Based Reimbursement

Abhinav Shashank
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Value-based reimbursement is probably the most talked-about notion in the healthcare industry. The announcement by the Department of Health and Human Services at the start of 2015 to link half of all traditional Medicare payments to a comprehensive value-based reimbursement model before 2018 concluded, followed quickly by the unveiling of MACRA accelerated the transition to quality care improvements and a value-based care world.

 

The Key to Value-Based Reimbursement

 
Discarding the traditional fee-for-service model and growing into value-based reimbursement is no easy task. A value-based reimbursement model requires advanced data analytics, end-to-end population health management solutions, comprehensive care management, and successful patient engagement. A health IT setup is crucial to value-based reimbursement in many ways:

  • Improve network volume: Maintaining and increasing network volume is pivotal to a value-based system. Healthcare organizations can leverage health IT to eliminate network leakages, improve quality, and learn about overhead cost drivers and streamline their operations and reduce waste.

 

  • Improve the efficiency of care delivery: The entire process of delivering care to patients and population at large is a cumbersome process. Health IT can reduce the tedium and the inefficiency in healthcare processes and not only improve performances and better the health outcomes but also understand the complete financial picture and improve cost margins.

  • Improve operational costs and resource utilization: In a value-based system, an understanding of return on investments and the cost structure on a granular level is indispensable. Health IT and advanced analytics can help organizations learn about the network waste, poor standardization of resources, and unnecessary cost drivers and counteract the inefficiencies in costs.

 

The Major Challenges in Implementing Large-Scale VBR

There are challenges in the initial stage of every implementation, and since value-based reimbursement is still in its infancy, there are a few but daunting challenges. Difficulties in transitioning to Value-based Reimbursement vary for every organization, but a few looming systematic barriers are on the frontlines that pose as roadblocks:

 

  • Tracking and harnessing relevant data: The healthcare space is filled with siloed datasets, and these disparate sources make it difficult to effectively capture and utilize data to derive meaningful insights about costs and outcomes. According to a recent survey, 35% of the providers are extremely concerned about their abilities to measure outcomes to receive payment under new VBR models.

  • Interoperability: Interoperability isn’t just connectivity between disconnected data systems. It goes beyond, enabling access to common data, workflows, enhancing communication, and strengthen common capabilities. In a survey of 220 providers, only 6% of them could exchange vital information to stakeholders on a different platform.   

  • Tracking widely-spread quality measures: Many of the value-based models – incentives as well as penalties rely on quality measures. So far, providers have been submitting their reports through reporting programs like IQR, OQR, PQRS, etc. Now that quality measures are exclusively tied to penalties and incentives, providers need sophisticated analytics to measure their performance.

A 7-Stage Infrastructure to Succeed with Value-based Reimbursement

 

Even though the transition-related barriers healthcare systems face may seem daunting, there is a simple 7-stage process to meet these challenges and accelerate the shift to value-based reimbursement.

 

  • Data Integration: There are exabytes of healthcare data, coming in from multiple sources, and holding meaningful information about billions of people. Undoubtedly, processing this huge amount of data needs substantial storage repositories and extensive data integration solutions. Providers can harness Big data analytics and storage repositories like a data lake to maintain their clinical, claims financial, and operational data and stream it into a single source and have cleansed, normalized data on their fingertips.

  • Performance Dashboards: Having access to a state-of-the-art performance dashboard is critical for providers to learn about their inconsistencies, monitor their system’s performance, and identify the areas of improvements. Healthcare dashboards give providers a consolidated view to track their inefficiencies and growth, their patients’ health status, monitor population health trends, and share this information across all relevant stakeholders to help them make strategic decisions well in time

  • Population Health Management: A successful population health management solution is integral to value-based reimbursement. It is no more an idea, but a clinical discipline that thrives on the vision to deliver end-to-end healthcare solutions to population and drive better health outcomes. It needs seamless data integration, risk stratification, effective care management, and enhanced patient engagement to have a complete understanding of the population at hand, and determine potential complications.

  • Care Management: Healthcare organizations in the pursuit of value-based care have to bring in a comprehensive care management solution, undertaking care coordination, chronic care management, and transitional care management for their patients to monitor and measure the quality of care services and plan interventions at the right time. Healthcare systems should be able to develop patient-specific care plans and obtain a real-time decision support to monitor the impact of care plans regularly.

  • Patient engagement: The patient experience of care is instrumental in a successful value-based reimbursement model. Healthcare organizations should focus on effective patient engagement that helps them pinpoint the gaps in care, develop robust care plans, ensure patient adherence, help them make right choices about their health, and make them a substantial stakeholder in the matters of their physical and behavioral health.  

  • 2-Way Interoperability: Interoperability has been one of the biggest challenges in the healthcare industry, despite several innovations and advancements. The lack of interoperability not only affects patients and healthcare organizations but also takes its toll on the overall population health. The lack of a common standard and stark differences in workflow make it difficult to bridge the gaps between healthcare systems and have to be addressed on the way to a true value-based care model.

  • 360 Patient Pulse: A real-time tracking of patients to continuously monitor and assess a patient’s health, learn about the underlying risk factors, and improving patient and organizational compliance is integral to delivering value-based care. Healthcare happens to be a dynamic industry, and multiple data sources come up every other day, making it difficult to keep track of patient data incoming from various sources. In a value-based reimbursement model, healthcare systems should have a unified view of patient data and address their needs, not as a member of the population, but as an individual patient.

 

The 7 Stages of Value-based Reimbursement with Datashop

 

Datashop, the operating system that powers healthcare is not just a collection of apps and tools but a comprehensive, end-to-end solution for delivering value-based care. Datashop counters the challenges healthcare organizations face, be it data integration or real-time patient tracking, in the most effective way. Datashop offers:

  • Datashop Connect: 45+ pre-built connectors to various EHRs and billing systems, bringing in data into a standardized and clean HL7 format.
  • Datashop Lake: An integrated big data lake as a storage repository, providing dynamic registries, measures, and performance dashboards.
  • Datashop Analyze: Powerful predictive analytics to identify the at-risk population, segmenting population, and monitoring areas for improvements.
  • Datashop Reports: Automated quality reporting for programs like MACRA, PQRS, HEDIS, etc.
  • Datashop Coordinator: An AI-assisted care management suite providing intelligent care plans and automated work queues for care coordinators.  
  • Datashop Refer: A referral management suite to help providers manage referrals effectively and reduce network leakages.
  • Datashop Engage: Patient engagement suite complete with machine learning algorithms and artificial intelligence to improve care plan adherence.

 

In a data-driven healthcare, quality and affordability are the primary emphases, and by leveraging Datashop, healthcare organizations can unify disparate data, manage care processes, and deliver the right care at the right time in a true, value-based ecosystem.

 

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