Value-based contracts offer provider organizations an opportunity to govern themselves in order to provide high-quality, cost-efficient care to their attributed members and take responsibility for determining the most efficient and effective use of healthcare resources. Much has been written on what a value-based contract is, what is included in a value-based contract, how to negotiate an effective contract and the different types of value-based agreements that exist, but less is written on how to make sure a provider group stays successful in a long-term agreement.

There are some papers that discuss the components that a physician must consider when contracting with an Accountable Care Organization that takes on the risk arrangement, including:

• Financial modeling and readiness assessment to move to full risk contracts
• Recruitment and credentialing of physicians to meet network adequacy
• Strong physician based governance structure
• Baseline quality and financial performance assessment as part of risk readiness assessment
• Review of existing and planned risk-based contracts to assess measures, data reporting and incentives
• Create model for distribution of earned incentives based on clear criteria
• Data analytics and reporting
• Structure and cost of membership into the IPA/ACO

There is however, little written about the importance of the kind of data used in these arrangements.

Data Types and Sources

What are the most important types and sources of data required to support success in a value-based arrangement?

1. Provider and eligibility data: Providing roster information and allowing appropriate accounting for member attribution to be able to identify members assigned to participating providers for accurate data mapping
2. Demographic data: This provides information about the attributed members, including age, sex, race, address as well as information on spouses or other caregivers, insurance information and other key elements for assisting in management of the member, payment for services and how to contact them
3. Claims data: This provides information about when and what kind of clinical services, pharmaceuticals , equipment and supplies the patient received. It also included the diagnoses to support the need for the member care interventions. This data can also provide process information that is important in many quality-related measures, such as the performance of a mammogram or a colonoscopy, or a visit to a primary care physician. The claims data can be used in concert with electronic medical record (EMR) data to develop targeted and prioritized gap closure lists and tracking progress around quality programs such as 5 STAR and clinical quality documentation.
4. Electronic Medical Record data: While the information above is critical, the data contained in the EMR is the key element, used in concert with claims data, in determining the outcome of any clinical workup or treatment for the patient. It is the source for definitive results that are elements of some weighted quality measures, such as HgBA1C or controlled hypertension measures. Until recently, EMR data has only been available when someone physically scours the EMR at a computer and extracts key information by hand. Now, with better data integration solutions enabling communication between EMR and analytics platforms, key elements from the EMR can be coded, extracted and delivered to analytics platforms that are also ingesting claims data for the attributed members and included in determinations for meeting both quality assurance measures and included in cost effectiveness calculations.
5. Care management, ADT feed, referral and social determinant, or health related social needs data: Social determinant or health related social needs (HRSN) data needs can be solved primarily through EMR data as coding for these areas improves. Other key data sources that are important to access as frequently as possible, preferably at least daily, are care management platform data on gaps closed and interventions made, ADT feeds to identify hospital admissions and ED visits for care transitions, and data from closed loop referral platforms for social or HRSN services. These data sources, normalized with claims, EMR and eligibility files, also greatly enhance the value of risk stratification and enable focused use of limited care management and social care resources.

Missing any of these data components can create an incomplete view of the success of a quality program, clinical documentation, total cost of care and leakage opportunities available in most value-based contracts and can negatively impact other aspects that are key to major processes in managing patients within the risk-bearing organization.

Gaps in care

Closing key gaps in care related to quality measures is an important aspect of almost all value-based contracts.  Data enables identification of gaps and determination as to whether a gap is closed and whether it was closed in a timely manner. Lists of attributed members who still need their gaps closed can also be developed and maintained. The primary issue here is the significant lag for claims data. It may take up to  four to six months for a payer to receive a claim, pay it and report it out. The information provided may no longer be actionable once it gets back to the providers because the gap may have been closed in the interim.

Providing data that is not accurate because of the delays noted can create abrasion and a lack of trust between payers and providers, , care team members and medical group leadership Data extracted from the EMR on a regular basis and normalized with the claims data can improve the timeliness and accuracy of reported information. This improves provider and care team satisfaction while also improving gap closure rates, especially those that may have a tight time limitation such as the need for bone density testing in patients with a history of a fracture within six months of the fracture.

Outcomes in care

Some quality measures require accurate measurements in order to meet the required standard. This includes successful control of diabetes as measured by a HgbA1c level below 9 and control of hypertension as measured by blood pressure less than 140/90.  These accurate measurements cannot be obtained from a claim form and must be gathered either by physically reviewing the medical record or by automatically incorporating the EMR in data collection and integrating it as part of the overall data collection process.

Clinical Documentation and Quality Improvement

Patients with severe or multiple chronic conditions utilize more healthcare resources and appropriately documenting those conditions make up the core of risk adjustment.  The assessment for patients with higher risk scores could affect the payments to the organization and make up for the higher spend needed to care for these patients. Coding appropriately is the primary process for capturing information on these patients and with integrated EMR data the ability to search for these diagnoses that may have been missed creates a safety net and enhances the ability to appropriately account for these sicker patients.

Accuracy

Depending solely on the use of claims data creates a bias that is developed with what goes into the claims. Errors are known to occur, and these can result in inaccurate reporting (diagnosis errors or differentiation between certain conditions, such as urosepsis versus urinary tract infection, for example). Having the ability to integrate EMR data allows for more accurate capture of clinical data can help in assignment of patients to population health management programs and improve their overall health and performance in risk management contracts.

Maximizing VBP Contract Revenue

Value-based payouts are based on the contract terms negotiated and performance against those for an attributed or assigned population. It is critical for providers to deeply understand all of the various aspects of their value based payment agreements, modeling the likely outcomes and ensuring alignment between the contract levers and ability to perform on the ground.

Contract targets should be based on timely data provided by payers, the availability of such data, timely and accurate, should be written into agreements. It should be timely and reported in a manner consistent with current standards. Attribution models should be well documented, clear and understandable. Measures should be based on third-party references and not based on measures developed by the payer solely for a provider or market. It is important to ensure validation of measures by outside experts. Measures should be able to be validated from the highest scoring level, all the way down to the patient level and confirmed in the patient’s medical record (EMR).

Data must always be risk and severity adjusted so that physicians and other providers have an understanding that the playing field is leveled, and sicker populations are accounted for.

Payouts on provider performance should be timely and sent out close to the time the reports are sent to the provider organization. This is so providers associate their performance with the payout. Those payouts that lag behind reporting and come out more than a quarter later have a tendency to lose their meaning to the provider group and are too late to allow for substantial and timely changes to the way providers practice and get rewarded for those changes.

COPE Health Solutions has a team of highly experienced experts who can support you with financial modeling, data analysis, data integration and delivery as well as risk adjustment for your population. If you are in need or help or are interested in learning more about COPE Health Solutions capabilities, please contact us at info@copehealthsolutions.com or 213-259-0245.