Your ACO just took on downside risk. What's your data strategy?

For most accountable care organizations, the near future increasingly includes downside risk — which might catch some ACO’s off guard.

This content is sponsored by Change Healthcare

ACOs are care networks that can consist of independent physicians, physician groups, hospitals, health systems and payers that join with a broad spectrum of nonacute providers — rehabilitation clinics, behavioral health counselors, lab groups — to coordinate high-quality, low-cost care for specific patient populations. In the past eight years, ACOs have grown to cover more than 32 million patients across every state in the country, according to a 2017 Health Affairs study.

At the top of ACOs' list of priorities: improved data analytics and reporting to help better manage chronic diseases, coordinate care and reduce practice variation, according to the Health Affair's study.

With ACOs highlighting risk management as a top-most concern, it's no surprise organizations are searching for greater visibility into patient health data through investments in health IT and data management. This article examines the role data plays in improving risk management for young and veteran ACOs, as well as key considerations for organizations ramping up their population health analytics programs in preparation for downside risk.  

ACOs are eyeing risk-bearing contracts

Approximately 26 percent of all Medicare ACOs participate in a downside risk model in January 2018. This is a 9 percent increase from 2017.

Driving the movement to risk-bearing contracts, in part, is many ACOs' realization that returns on investment generated through MSSP Track 1 — a non-risk-bearing model — fell short of what organizations sought to recover upfront costs and remain profitable.

"Effectively creating the infrastructure to manage a population is not something you do in a year," says Dan Underberger, MD, clinical solution executive and medical director of the clinical analytics team at Change Healthcare. “It takes several years and significant sunk costs to get the analysts, care management team, and technology in place. Many of these entities are two, three or four years into the [MSSP] program, when they begin to see what works and what doesn't. They've got cost of care as low as they possibly can, and are starting to get a sense of what their capabilities are with their performance improvements."

Mature ACOs aren't the only organizations eyeing risk-bearing contracts. Even historically risk-averse organizations are considering a downside risk strategy, largely driven to the table by CMS. Eighty-two ACOs in MSSP Track 1 since 2013 will decide between engaging in downside risk by 2019 or forfeiting their participation in the program. The most recent MSSP performance results indicate many remaining Track 1 ACOs are mature enough for downside risk. Approximately 91 percent of Track 1 ACOs would have financially benefited from assuming downside financial risk in the Track 1+ pathway based on 2016 performance data, according to a 2018 Avalere study.

ACOs new to MSSP are also being pushed toward downside risk through MSSP Track 1+. The program, which builds on the first track iteration, is designed to encourage less risk-sophisticated, rural and smaller ACOs to experiment with risk by offering more limited downside models compared to tracks 2 and 3. The Track 1+ program boasts 55 ACOs in 2018, including major names like Cleveland Clinic.  

A crucial component to understanding operational needs in risk-based contracts is moving beyond EHR systems to incorporate population health management solutions. Eighty percent of ACOs reported using some type of population-based analytics solution in 2017, according to a poll by the National Association of ACOs and Leavitt Partners.

Despite the near ubiquity of population health management systems, most ACOs have room to grow when it comes to laying the groundwork for population-based analytics. Integrating a population health management solution into an EHR is but a single component of an effective analytics initiative, especially if ACOs intend to use their analytics for predictive modeling down the line.

Equally, if not more important than sophisticated IT solutions are high-quality, comprehensive data sources and standards that promote data integrity and foster physician trust in data insights. Because data analytics are only as valuable as the insights they yield, managing data to ensure it's secure, available, reliable and actionable is a top priority for organizations looking to use data in risk management.

With ACOs highlighting risk management as a top-most priority, it's no surprise organizations are searching for greater visibility into patient health data through investments in health IT architecture and data management processes.

State of HIT and data processes in ACOs

Dr. Underberger discussed five considerations for ACOs looking to mature their data analytics programs in preparation for downside risk.

1. Data acquisition gaps. Acquiring data from each hospital, physician group and provider touch point within an ACO's network — from independent physicians practicing in multispecialty facilities to diagnostic labs — is key to building a valid, comprehensive snapshot of care management.

"You can't fill patient care gaps until you fill the data acquisition gaps," Dr. Underberger says. The data necessary for ACOs to evaluate patient health risk lives in a range of disparate systems in a multitude of formats — including data found in EHR and practice management systems, claims data gathered from clearinghouses and health plans, and increasingly, patient-generated data from devices and even genomic tests.

Executing this fundamental need for data acquisition brings ACOs face-to-face with one of health IT's greatest stumbling blocks: interoperability.

2. Interoperability challenges. Lack of interoperability standards across clinical and financial data sources adds enormous time and cost to data acquisition. Even without bringing financial data into the mix, exchanging data between discrete EHRs still presents technical difficulties. In fact, ACOs identified data extraction from EHRs as a leading problem due to poor interoperability standards, high labor costs and irreconcilable vendor business practices, according to a 2016 AAMC study.

The challenge is two-fold. For one, providers comprising ACOs use 13 different EHR platforms on average, according to the Health Affairs study. This complicates both the flow of information and the format in which it is stored.

Further intensifying the issue, the AAMC study also found an inverse correlation between ACO growth and data integration. As ACO networks added new provider teams and expanded their continuum of care, the likelihood of data returning to the primary care team, which is responsible for coordinating care and planning interventions, fell off significantly.

Poor interoperability between clinical sources means data acquisition processes, for most ACOs, are highly manual. "To get practice data outside the hospital, ACOs have hired a lot of [full-time employees] to do the legwork — physically going to provider groups to track down claims and clinical information," Dr. Underberger says. Maintaining the internal employee infrastructure necessary to conduct acquisition at scale across a network simply isn't in the budget for many ACOs, driving provider groups to increasingly outsource this component.

3. Time sensitivity. It is no easy task for ACOs to get the right data from disparate systems to data analysts, and then to care management teams, in a timely manner to positively affect patient care. This is especially difficult when it comes to ingesting claims data.

Claims data drawn from an ACO's member population can provide a wealth of insights. EDI 837 (pre-adjudicated) claims can be particularly valuable. For a single patient-provider interaction, 837 claims include patient demographics, the services and patient's condition(s) for which treatment was administered, the provider and date of service(s). This information helps ACOs track patient care across the network to ensure patients requiring greater degrees of medical attention, such as those with chronic diseases, receive appropriate care when and where they need it.  

Despite claims data's value, few health systems are experienced in using it, largely because they wait (30 – 60 days) to receive adjudicated claims information from payers.  By the time that data is presented to care coordinators, the window of opportunity for patient intervention has already passed or is no longer relevant.

4. Data security. Controlling access to data is essential for healthcare organizations, as some data are appropriate for one organization to see at one time, but may be illegal for another partner organization to see at a different time.  

Consider EHR data shared between a hospital and physician group participating in an ACO. It's helpful and appropriate to share patient information for that specific population when making healthcare decisions, such as planning interventions or offering new services. However, it is inappropriate for a hospital physician to access all of the patient records in the physician group's EHR.

Protecting physicians' financial information is equally important when sharing data.  

"In addition to gaining physician buy-in, you need to have physicians' confidence that you have the appropriate data security policies in place," Dr. Underberger says. "Most physicians are reluctant to share their claims information with other institutions because of the risk their billing information may be exposed or leaked."

5. Data aggregation and traceability. Capturing data is the first step toward evidence-based medicine. But data is only as helpful as the insights it yields, and it often requires a significant amount of rework before it can be combined and used to make clinical, operational or business decisions.   

Different types of information exchanged for ACO care coordination may require the use of different types of documents. For example, a behavioral health assessment may be captured and sent as a PDF, a nurse's patient notes may be stored as unstructured free-text, and  a laboratory result may be sent in a HL7 message or in a cCDA (via an EMR). 

As is, data scientists at health systems spend a substantial amount of time making data usable. The granular nature of the job — what data scientists call "data wrangling" or "data janitor work" — requires enormous labor and cost. Data scientists report spending up to 80 percent of their workday collecting and preparing unruly, disparate healthcare data, according to a survey conducted by The New York Times. That leaves only 20 percent of the day to use and analyze data to drive insights that bring positive change to organizations.

To help support data analysts and acquisition teams, ACOs see value in partnering with third-party vendors.

Advantages of partnering with third-party vendors

ACOs are strapped for resources when it comes to supporting data analytics. ACOs reported spending an average of $600,000 on operating expenses for health IT, analytics, and reporting, which is relatively low compared to the reported average investment of $1.1 million on care management, according to Health Affairs.

"Our clients say their biggest problem is internal IT staff being totally overwhelmed with the task at hand and in need of help," Dr. Underberger says. "Most of these organizations only have a handful of data analysts, and the data acquisition teams they do have are mostly concerned with their own network facilities. They haven't even begun wrapping their arms around what it takes to process external data. That's why many ACOs are going to third-party vendors [for support]."

Change Healthcare helps ACOs prepare their data for improved risk management. The IT and consulting company is in the process of building and rolling out a data-agnostic platform with security policy infrastructure that accepts data from any source, including individual physician data and claims information from a multispecialty out-of-network facility. The platform then aggregates and normalizes the data, applies patient identifiers to map and trace patients across discrete IT systems, and enforces rules to improve data presentation for end users.

In addition to helping ACOs establish secure, standardized data processes, Change Healthcare offers analytics support services to better mine data for actionable health insights.

"We have an analytics team that helps ACOs analyze their own data in a way that is more visual and meaningful," Dr. Underberger says. "For instance, we are developing a measure service engine that calculates a patient’s quality measure compliance score based on the type of data we receive. In addition, our analytics team can help configure the solution to attribute these patients to the most appropriate providers within various practices." 

Conclusion

In an ACO, several organizations practicing along the continuum of care capture various types of information, in distinct systems at different times. Organizations need a defined plan to put their data to work across the enterprise if they are to survive in the challenging healthcare landscape.  This includes building the skills and processes to transform raw data into information that drives earlier patient interventions and better health outcomes. 

Understanding known data barriers and viable IT management solutions are the first steps in choosing the right analytics strategy to achieve specific care coordination objectives. Third-party vendors can be extremely valuable partners in helping ACOs control analytics and better manage risk by supporting manual data acquisition processes, overcoming interoperability challenges, unclogging the flow of information, ensuring security compliance and supporting data aggregation and lineage traceability.

More articles on ACOs:

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