Skip to main content

Three Steps to Successfully Participate in Primary Care First

There is renewed emphasis on advancing primary care. Primary care serves as the front door to the overall healthcare system, and early and accurate diagnosis can lead to fewer hospital admissions – a metric that has become especially important during the pandemic.

With the January 2022 launch of Primary Care First Cohort 2, it is important to review how the program works and what steps healthcare organizations should take to ensure the best outcomes.

To be successful, PCF participants must pay special attention to three aspects – tracking beneficiary population, obtaining physician buy-in, and ensuring timely access to performance metrics.

Tracking Beneficiary Population

Tracking patients participating in the PCF program is necessary to build patient navigation processes and monitor care outcomes. However, this can be challenging for many providers as internal EMR solutions do not provide complete data across the entire continuum of care for all providers that a patient may see. This limited view can hinder understanding of service utilization, adverse outcomes, and sources of leaked primary care visits. If practices lack the resources and expertise to collect and analyze additional sources of data outside of the EMR, organizations can have trouble making necessary changes for PCF success.

To achieve the best PCF outcomes, primary care providers must rely on solutions that complement EHRs and enable clear visibility into comprehensive data across the continuum of care to analyze leakage, patient risk profiles, and acute hospital utilization.

Physician Buy-In and Engagement

It is critical to have enterprise-wide buy-in and engagement to support PCF participation. This often is a roadblock to success, as clinicians are pressed for time, especially during the pandemic. That factor, as well as growing staffing shortages and diverted staff resources, can limit the ability to meaningfully analyze patient data and identify emerging insights from PCF participation.

To be successful, there is a strong need for data expertise to process, adjudicate, and normalize data to make it readily available for analysis by value-based care specialists and care teams. If your data processing resources are scarce, take steps to build a strong data infrastructure to ensure PCF program success.

Timely Access to Performance Metrics

Rules and requirements related to quality measurement represent another crucial but challenging aspect of program participation. Providers must continually track their performance towards the goals of the program and assess compliance with APM rules. Although CMS tries to address this by providing summarized results for participants, it is usually not detailed enough on its own, leaving providers with a data void.

To meaningfully improve care delivery and calculate financial impacts, healthcare providers must assess data on a more detailed level in addition to what is provided by CMS on a summary level. This requires in-depth access to detailed claims data and patient information. To reach the best outcomes, healthcare providers must balance the challenges of internal program management versus partnering with an APM program management partner. In many cases, a collaborative effort can provide the data management and analysis capabilities that many providers lack, improving the likelihood of program success.

Investing in Data Analytics Is Critical to PCF Success

To ensure an effective and sustainable PCF program, healthcare organizations have to place a strong focus on harvesting data analytics insights to better respond to payment policy changes and market dynamics, and inform practice transformation activities within Medicare’s value-based programs. Strong data enhances the ability to evaluate financial and quality outcomes within all payer scenarios, enhancing the probability of improved patient outcomes and decreased expenditures. To learn more about how your organization can leverage data analytics for PCF success, click here.

Comments

Popular posts from this blog

Patient safety culture survey: Why collect data?

The Agency for Healthcare Research and Quality defines patient safety culture as "the extent to which an organization's culture supports and promotes patient safety." Patient safety culture is influenced by the values, beliefs and norms of healthcare practitioners and other staff. Since these concepts tend to be abstract, organizations looking to improve their patient safety culture must focus on identifying and measuring patient safety-related behaviors.  In this introductory blog, we'll touch on the importance of patient safety data and how it can help create a baseline. From there, you can gain a clearer idea of how to benchmark your facility to create effective patient safety culture improvement strategies.  Why collect patient safety data?  The best way to examine patient safety culture at the department, organization and system levels is to measure data. An organization can implement many different patient safety culture strategies. However, for them to be most

CMS Enhancing Oncology Model Updates: RFA Issued for Second Cohort

Key CMMI updates to the EOM  The Center for Medicare and Medicaid Innovation (CMMI) released exciting updates to the Enhancing Oncology Model (EOM) along with a new opportunity for a second cohort of participants.   The EOM aims to enhance the quality of care for cancer patients while reducing costs under the Medicare fee-for-service program. The updates come on the heels of lower-than-expected model participation .   This blog will discuss key EOM updates, application details, eligibility requirements and important deadlines.  New cohort opportunity  Request for applications: CMS issued an RFA to recruit a second cohort of participants and payers for the EOM.  Timeline:  Second cohort start date: July 1, 2025  Second cohort end date: June 30, 2030  Initial performance period start date: July 1, 2023  Model test end date for all participants: June 30, 2030 (extended from June 30, 2028)  Notable changes to the EOM model  Model extension: The model's duration is extended by two yea