Despite a party change in the White House, the government will likely continue moving toward performance-based healthcare with a greater focus on outcomes. Under such models, hospitals’ reimbursement is directly tied to patient outcomes, meaning outcomes management is critical to a healthcare organization’s success in a value-based world.

In 1988, Paul Ellwood Jr., MD, also known as the father of the HMO, coined the term “outcomes management” and defined it as “a technology of patient experience,” that estimates as best as possible the relation between medical interventions and health outcomes in a language understandable by patients. We have done little in the past 30 years to achieve Dr. Ellwood’s vision of routine outcomes management, said Richard Gliklich, MD, CEO of OM1, a Boston-based data analytics company that supports outcomes management. But that is all changing and outcomes management is again at the forefront.

To make outcomes management work in today’s value-based healthcare environment and clinical workflow, it needs to include four key elements:

  • Patient-reported and clinical outcomes measurement
  • Contextual and predictive analytics
  • Data integration across systems
  • Automated data collection and interaction
  • Data accessibility, reporting, and interaction across an enterprise and different care settings

Many hospitals have yet to accomplish the full spectrum of outcomes management, but with the emergence of new technology and data analytics tools, more hospitals are seeking outcomes management service partners, said Dr. Gliklich. OM1 solutions help hospitals leverage predictive analytics as they step into bundled payments and manage other value-based care programs.

During an executive roundtable at the Becker’s Hospital Review 5th Annual CEO + CFO Roundtable in Chicago on Nov. 9, 2016, Dr. Gliklich delivered a presentation on how to measure outcomes, manage risk and avoid penalties by leveraging patient reported and clinical outcomes data.

Dr. Gliklich discussed value as outcomes divided by costs. To keep the equation standardized, outcomes should be defined consistently across the industry. He said outcomes can be measured by survival, clinical response, events of interest, patient-reported outcomes and resource utilization.

Outcomes management is particularly valuable to organizations that are taking on alternative payment models under pay-for-performance, such as bundled care.

Bundled payments allow providers to transition from fee-for-service to a total-cost system that may be tailored to fit an organization’s needs. Currently, three national bundled payment programs are in place or proposed: Comprehensive Care for Joint Replacement model (2016), Oncology Care Model (2016) and Cardiac Care Model (2107). Traditionally, these programs were based on a collection of claims data, but now the programs integrate other data, such as clinical elements and patient-reported outcomes.

“Outcomes management may help determine the winners and losers in value-based care,” said Dr. Gliklich. This is because under a bundle, the anchor hospital is responsible for patient outcomes from admission to 90 days post-discharge. Hospitals that end the performance period above the fixed target price repay Medicare the difference and those ending below the target price are able to share in the savings.

“Bundled payments are coming forward as the solution that seems to have the most opportunity for success; and the reason is not that it’s brilliant — it’s that it’s gradual,” explained Dr. Gliklich.

Organizations finding success under the CJR will lower costs below the target price and avoid high-cost events in a systematic way.

The role of data analytics systems in bundled payments
Leveraging data also proves critical in a bundled payment program. Data may lead to cost-savings through predictive analytics by enabling providers to identify risk and intervene before high-cost events occur.

Dr. Gliklich defined data analytics within the following categories:

  • Descriptive analytics: Look back in hindsight
  • Diagnostics analytics: Root cause analysis
  • Predictive analytics: Determining what will happen
  • Prescriptive analytics: Make the event occur or avoid the event

“I like to think of predictive analytics as answering the ‘which’ question,” Dr. Gliklich said. New requirements may appear unexpectedly, so successful organizations will put in a lot of preparation work in infrastructure and technology.

Key data challenges
Hospital executives chimed into the discussion, illustrating the various data challenges their organizations are facing as they tackle outcomes management initiatives.

The chief quality officer at a Level 1 Trauma Center in the Midwest said his hospital has an abundance of data, but most of the analysis applied is retrospective. “To me, what is really intriguing is this ability to be proactive rather than reactive,” he said.

The CIO of a parent company owning two hospitals with a combined 340 beds in the eastern United States agreed, saying the hospitals also struggle with acquiring valuable data beyond that which is collected in the EHR system.

“The concern that we keep wrestling with is how to get access to the right data,” he said. “A lot of patient outcomes are affected by their home life…We often have no data for that.”

But even if the data is available, hospitals are considering how to actually get the right data in front of the right people at the right time.

“The ability to have actionable data early [is a challenge],” noted the president and CEO of a 15-hosptial, 45-clinic health system in the Midwest. “When I look at the predictive analytics and the prescriptive analytics, I think there’s tremendous thirst for that. It’s just all a matter of how can we get that in front of the right people early?”

To this point, Dr. Gliklich emphasized providing the analysis to the care decision-makers at the time a patient presents preoperatively: “We find that you actually have to give [the insights] to more than one person to actually have action.”

Ultimately, outcomes management combined with predictive analytics presents strong opportunity for a hospitals’ return on investment. Predictive capabilities, which can be integrated quite inexpensively, support outcomes management by identifying patients at risk for complications or other events and help clinicians know when to intervene. Under alternative payment models such as bundled payments, a robust predictive analytics system and outcomes management model can help hospitals produce the best possible outcomes for patients, thereby securing favorable reimbursement from payers.

Source: ASC Communications