Leveraging Existing Technology to Proceed Along the Risk Continuum

Technology is essential when participating in any form of alternative payment models (APMs). However, for organizations looking to move into these arrangements, they often fear that they will need to make drastic changes to existing technology platforms and electronic health record (EHR) systems, so that effective data exchange with key partners or other practices can be enabled. 

The good news is that organizations should not have to change their EHR to thrive in these arrangements; instead, they need to identify and implement key functionalities to effectively capture, understand, and use data. These capabilities will facilitate better clinical decision-making, reduce care costs and mitigate risk—essential strategies for successful APM participation. 

First, organizations need to efficiently acquire data from outside your network. 

By collecting data from external sources, such as data from a health plan, health information exchanges (HIEs), other health care providers in or out of your network, and discharge notes from local hospitals, organizations can create a full picture of the care, treatment, and services the patient has had and is continuing to receive. Although providers can collect this information manually, the effort is time consuming and requires a lot of effort, taking time away from direct patient interactions leading to patient and provider frustration. On the other hand, a software platform that captures and aggregates data automatically from multiple sources - including data from disparate sources such as labs, pharmacy and medical claims, physician EHRs, and inpatient EHRs - can increase efficiency and reduce the risk of missing critical care interactions, such as ER visits or diagnostic tests that occur outside a patient’s typical care venues. 

Next, organizations must patient-match and normalize the data to make it useful. 

Data is meaningless without the proper context. Automated platforms should not only collect the data but normalize it to make it comparable, shareable, and meaningful. This supports the creation of a longitudinal patient record, which can improve care quality while limiting waste through duplicative procedures.  

Third, organizations must act on the data to better manage population health. 

Having high-quality, normalized data is just the start of the work. Organizations must also have a mechanism that drives automation while identifying potential risks and opportunities through machine intelligence coupled with the enhanced data set now available. Although they could task internal staff with this activity, it can be time-intensive, and their staff may not have capacity due to competing priorities. Moreover, effective population health management requires a certain skill set and finding staff with the necessary data analytics and population health expertise may be challenging. 

A viable alternative is working with an expert partner that has deep experience in data-driven population health management solutions. These teams should support the organization’s move to using an EHR-agnostic technology platform to uncover a myriad of improvement opportunities. For example, our teams at CareAllies can help improve accuracy and consistency with coding. By using technology that captures and normalizes integrated data from claims, admission-discharge-transfer (ADT) feeds, laboratory systems, and EHRs, the CareAllies team was able to accurately assess chronic condition opportunities to the highest level of known specificity to give the providers and their health plans the assurance of accurate reimbursement. This resulted in significant improvement opportunities for a 55-physician provider organization, which had been highly engaged and organized, but had relied on a traditional manual approach. Because of the technology, the team uncovered the potential improvements in a matter of weeks, as opposed to following an extensive auditing process that would’ve involved care coordinators, coders, and the health plan. 

Using a normalized data set from multiple sources, organizations can also enhance chronic care management, which often impacts reimbursement in an APM. For instance, by creating standing orders based on clinical diagnoses and best practice protocols, technology can flag patients with those conditions in the practice EHR. The CareAllies team helped to coordinate care for these patients with chronic conditions to get the care they needed more reliably and prevented those conditions from worsening—all while lessening the administrative burden on primary care physicians.

Another benefit of this approach for aggregating data is that organizations can better manage the care for individuals who have multiple conditions that require attention. Typically, poly-chronic patients make up about 4% of a provider’s patient panel, and these complex patients should not be treated using standing orders. Multiple chronic conditions can have varying impacts on the patient and require more customized care. By leveraging technology to automatically identify and streamline outreach to these patients, the combined care team can more efficiently get the patients into the optimal provider’s office and enable better coordinate care delivery for the patients with the greatest needs to increase their overall quality of care while decreasing unnecessary cost.  

A combination of technology and expertise is a winning strategy 

Realizing success with APMs requires a combination of robust technology and knowledgeable staff familiar with population health management tactics. By partnering with a company that can both implement an automated data platform and offer seasoned experts, organizations can more efficiently identify and respond to opportunities to optimize care, reduce risk, and drive efficiency. Not only will this help improve their performance with their current APM, but it will also allow them to drive better care and outcomes for their patient population while enabling room for growth, laying the foundation for even stronger financial performance as they proceed along the risk continuum. 

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