How to Avoid Squandering Data Resources for APP Reporting

Article

Does your transition to Alternate Payment Model Performance Pathways (APP) quality reporting have your ACO scrambling for data? No surprise.

Does your transition to Alternate Payment Model Performance Pathways (APP) quality reporting have your ACO scrambling for data? No surprise. For years ACOs have relied on the CMS Web Interface to identify the small sample of patients required for quality reporting, deploying nurses to complete reporting through chart checks. That world is ending.

With APP Reporting, the big challenge for multi-practice ACOs is to integrate patient data from different EMRs to meet all-patient requirements, while not duplicating patients by combining all practices' data. Patients in quality measures can only be reported once. Even single-EHR ACOs will have issues stemming from variations across practice registration processes.

Here’s a warning about aggregating data for APP Reporting: Don't be tempted to adopt a simplistic solution that won't really work and will cost you more than the data is worth. There are multiple ways of capturing data from EMRs, but not all EMRs have the same methods—or the same price tag.

Beware the Illusion of the All-QRDA Solution

Many ACOs have zeroed in on QRDAs as an option for APP reporting. Some EMRs have an internal measures engine that identifies values for patients eligible for quality measures (QRDA 1s) and converts those into quality results (QRDA 3s) for eCQM reporting.

If your ACO is going down the QRDA path, QRDAs appear to offer some key advantages:

  • Eligible patients are identified by diagnosis codes (and exclusions);
  • Clinical values are assembled for measure completion and performance.

Unfortunately, the reality doesn't meet the vision. Here's why:

  1. Not all EHRs can export QRDA 1s.
  2. Even for EHRs that can produce QRDA 1s, staff expertise is required.
  3. QRDA 1s are one of the most expensive options for processing the data, three to four times the cost of the less expensive HL7 or flat files.

Feasibility of retrieving QRDA 1s is uncertain for untested older or cloud-based systems. Some systems have no means to report out individual patients easily, even if they are ONC-certified for quality reporting. Certification requires that the EHR can create an aggregated QRDA 3 report to submit to CMS. But that does not mean that the system can also externally generate QRDA 1 files. And even if it is internally generating QRDA 1s, it’s often difficult for technical support staff to produce that feed.

Focus on Value for Your Long-Term ACO Data Strategy

Your solution to data aggregation needs to focus on "value," which means that your data aggregation should also garner the most return for your investment. There are two issues to consider: cost of getting the data, and how many solutions that data can deliver for your ACO.

First, examine your costs. The costs of data extraction and processing will vary by system, methodology for data collection, processing, and storage. For ACOs with a lot of independent physician practices on multiple systems, it’s hard to estimate the price tag in advance. But QRDAs are heavy users of processing capability because of their XML format, in addition to each record’s data volume. Extra staff are required to manage data processing queues for QRDAs. Bigger systems like Epic usually have much more data in the QRDA 1 file than smaller systems.

Don't overspend on QRDA processing because you think it standardizes the data. It doesn't. There’s data variation among QRDAs from different providers and from different systems. More data in the record just costs more.

Second, evaluate your need for data for all your ACO initiatives. Single-use data aggregation is expensive. While QRDAs do indeed create a dedicated data source for quality measures, returns are limited, at best.Most organizations will benefit by using the greater number of CQM measures available, and the technology will do the heavy lifting of either eCQM or CQM reporting. creating no real advantage for eCQM reporting.

More important, many ACOs have lacked the capacity for significant cost reductions and coordination activities because of data insufficiency. Your best approach is to maximize the amount of data you are gathering each time to build a patient-centric database. This is your path for evaluating patient outcomes and identifying gaps, health inequities, and costs––and for targeting performance improvements. You can centralize your data aggregation while dispersing data to various functionalities as needed.

How to expand the amount of data available to your ACO while fast-tracking aggregation? Deploy a hybrid approach. QRDAs may be the only method of obtaining data from practices with smaller systems. For larger systems and large groups, flat files or an HL7 (including FHIR) feed will be both economical and data-rich.

Your optimal path to data sufficiency will combine both provider data and payer data, especially CMS claims. Don’t make a false start. Avoid a single-use data aggregation approach that may not work or fails to achieve APP Reporting compliance and better performance.

CEO and Co-founder of Roji Health Intelligence, Theresa Hush is a health care strategist and change expert with experience across the health care spectrum, including public, non-profit and private sectors.

Related Videos
Lawrence Eichenfield, MD, an expert on atopic dermatitis
Video 5 - "Obstacles in Adapting Diabetes Technology to Individual Needs" - 1 KOL is featured
Lawrence Eichenfield, MD, an expert on atopic dermatitis
Lawrence Eichenfield, MD, an expert on atopic dermatitis
Video 4 - "The Impact of Continuous Glucose Monitors & Digital Solutions on Diabetes Care"
Video 3 - "The Pivotal Role of Patient Engagement and Education in Achieving Optimal Diabetes Outcomes"
Lawrence Eichenfield, MD, an expert on atopic dermatitis
Related Content
© 2024 MJH Life Sciences

All rights reserved.