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AHIMA’s Position

AHIMA supports the use of policy to promote innovative payment and care delivery models that leverage accurate, timely and complete health information, as well as technology in new and innovative ways. Health information (HI) professionals have extensive knowledge and expertise to contribute to policy discussions that support the shift towards value-based care. To support the advancement of innovative payment and care delivery models, AHIMA believes that public policy must:

Policy must consider supporting the “triple aim” – improving the individual experience of care; improving the health of populations; and reducing the per capita costs of care for populations.

High levels of data quality and integrity are a necessary component of value-based care models. Policy should work to ensure that the data used in value-based care models apply relevant coding standards and guidelines. Policy should also encourage the leveraging of non-traditional datasets including social determinants of health data as part of new value-based care models.

Interoperable, electronic reporting for value-based care models requires investment in clinical infrastructure, sufficient staff expertise, and harmonized reporting and measurement standards. Modern technical standards and open application programming interfaces (APIs) are needed to enhance data sharing and improve automation of reporting requirements. Measurement standards that are incorporated into value-based care models must also be developed, tested, piloted, and deployed broadly as part of a transparent process. Technical solutions are also needed to allow for more accurate patient identification and patient attribution, as ongoing challenges related to patient identification and attribution can hinder both quality improvement and cost containment efforts.

Models must reflect how information flows through the healthcare system, the technical infrastructure that is needed, and the role HI professionals can play in helping to leverage accurate, complete, and timely information
for value-based care models. Models must prioritize patient care versus increasing administrative or compliance burden on patient and providers.

Policy must ensure during testing and design that both models and individual measures are not systematically biased. Models should also be designed, tested, and deployed in a transparent manner and account for the needs of a variety of communities. Policy should encourage meaningful participation across clinician type, specialty, and geography.

New value-based care models should be sufficiently tested and piloted prior to widespread deployment and relevant findings should be made publicly available. In early phases, risk-bearing models should be deployed in a manner that allows for voluntary participation. Policy must also ensure that new value-based care models appropriately align incentives involved in risk bearing models to advance better integration of care without jeopardizing patient access to care.

Innovative value-based care models should promote patient engagement and not arbitrarily limit both patients and
providers by geography or modality. Policy must encourage the incorporation of telehealth and remote patient monitoring, as part of new value-based care models provided that the technology is safe, effective, appropriate, secure, interoperable, and can be integrated into a provider’s clinical workflow. Policy should also account for workforce training needs to ensure members of the workforce are sufficiently trained to leverage new technical capabilities.

Sharing of health information across payers and providers as part of new payment and delivery models requires careful consideration of privacy issues, including ensuring that only the minimum necessary information is shared, and uses beyond the specific transaction are limited. Addressing privacy and security successfully involves leveraging both technical and operational solutions that support clear policies that are consistent across all actors.


Healthcare has been in a constant state of reform over the past century. In recent decades, conversations around reforming the healthcare system have increasingly focused on “value-based care” as stakeholders have worked to reform the healthcare system so that it more closely reflects the “triple-aim.” The “triple aim” is a framework for the healthcare system that emphasizes improved patient experience, improved population health, and reduced per-capita
healthcare costs. As such, stakeholders across the healthcare system have emphasized the need to prioritize value-based, quality healthcare over a payment and care delivery system that bases reimbursement on the volume of visits or services provided. Value-based care can refer to a wide range of care delivery models, aimed at improving care quality for individuals and populations, while controlling healthcare costs.

For many providers and healthcare facilities, their participation in value-based care models are critical elements in strategic planning, with the desire to both improve the healthcare system and reap the incentives inherent in programs that tie reimbursement to performance. For payers, value-based care models represent a chance to control rising healthcare costs, while improving beneficiary satisfaction and health. Value-based care models also create opportunities to more closely tailor care to the needs of both individuals and populations by addressing issues associated with health equity and social determinants of health. In recent years, Congress has passed several key pieces of legislation aimed at transforming the healthcare system to one that emphasizes the use of technology and places a greater emphasis on value including: the Medicare Improvement for Patients and Providers Act, the HITECH Act, the Affordable Care Act, the Medicare Access and CHIP Reauthorization Act (MACRA), and the 21st Century Cures Act. These pieces of legislation and subsequent regulations have substantially realigned incentives for providers and clinicians across the healthcare landscape to participate in new payment and care delivery models that are aimed at improving quality of care and/or reducing costs while leveraging technology. In recent years, private payers have also increasingly moved towards participation in value-based care models.


Key Points

Advancing value-based care models is likely to yield numerous benefits to stakeholders across the healthcare system. These benefits include:

  • Improved quality of care, patient experience, and outcomes. Transparent, quality driven, healthcare payment and delivery models will improve patient outcomes and ensure stakeholder decisions are better aligned with positive incentives. Transparent payment and delivery models will also drive quality improvement as program participants better understand how they can impact metrics and proactively work to improve performance.
  • Decreased inappropriate or otherwise avoidable utilization of healthcare resources and services that in turn can reduce strain on the healthcare workforce, improve patient access to care, decrease wait times, and improve trust between patients and providers. This also will promote continued financial viability of healthcare programs through a greater ability to control costs.
  • Better alignment of positive incentives for payers, providers, and technology developers to support the flow of clinical and operational information.
  • Improved identification of emerging opportunities for clinical improvement, stemming from data collected for the purpose of value-based reporting.


As the healthcare delivery system continues to transition to a system that incentivizes value over volume, certain challenges and barriers must be addressed:

  • Providers and patients may be hesitant to participate in value-based care models. Providers may lack the necessary resources, sufficient staffing and expertise, and the technical capabilities needed to collect and report measures, or meaningfully improve quality performance under certain models. Patients may also suffer from low health literacy and may not understand the implications of participation in value-based care models.
  • Mistrust from historically marginalized communities may hinder participation including data sharing that is needed for care coordination and to promote equity within new and innovative care delivery models.
  • Value-based care models may be systemically biased and accurate risk adjustment can be hindered by lack of access to administrative and claims data. Certain providers and facilities may lack the required infrastructure, such as advanced data analytics and population health management tools, to allow for the effective management of risks associated with care and costs for patients in certain populations.
  • Lack of accurate patient attribution can hinder accountability and cost containment. A wide range of patient attribution models have historically been used to assign risk to provider entities under value-based care models. Outcomes on performance measures can also vary dramatically depending on the way that patients are attributed to providers. Inconsistent or inaccurate attribution may cause providers to be held accountable for outcomes that are not directly under their control. This variation can reduce provider willingness to participate in value-based care models and lead to the proliferation of models that do not accurately capture the value of care being provided.
  • The lack of a national framework for patient identification poses significant challenges when attempting to accurately account for individualized cost and quality-related outcomes. Patient misidentification can include duplicate records and overlaid records, resulting in interoperability challenges between EHR systems.
  • It is possible that successful participation, as defined by improvements on key cost and quality metrics, may take several years as stakeholders implement new policies, optimize resource allocation, and implement lessons learned from participation in previous years. Implementing models in an iterative manner can support long-term programmatic success, participation, and sustainability, but may delay desired outcomes.
  • Lack of standardized concepts and definitions to capture and document social determinants of health data elements. A limited number of Social Determinants of Health (SDoH) diagnosis codes can currently be transmitted in a payment transaction. As a result, existing data sources are not robust enough to support risk adjustment methods that reflect patient complexity and the quality of care being delivered as part of value-based care models.

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