Closing the Loop

in healthcare SM

Loopback Analytics enables health systems to proactively identify at-risk populations, match patients to appropriate services and evaluate the impact of interventions on clinical and economic outcomes. The platform allows organizations to selectively and securely share data and benchmarks with network partners to coordinate care beyond the walls of owned facilities. Real-time analytics monitor patients as they move across the care continuum and flag patients of rising risk for early intervention. Data collected from patient engagement creates a feedback loop to inform machine learning models and enable continuous improvement. Areas of focus include specialty pharmacy, medication adherence and provider networks.
The Loopback Platform brings together technology to proactively identify populations, match them to one or more intervention programs, engage patients using Loopback’s Navigator workflow or workflow native to the EMR, and evaluate the efficacy of each intervention program. By working these activities in concert, the Loopback Platform can support a process of continuous improvement.

Health Systems are continually evolving their underlying systems and processes which in turn impacts the availability and quality of the data to run the learning loop. New data opens the door to new data markers. Better data coverage enables more accurate prediction and risk adjustment models. Improved timing reduces the reliance on proxy data and speeds decision making. Data sharing agreements enables the development of more accurate models, benchmarks and best practices by leveraging data outside the walls of a single organization.

1. Identify

The ability to proactively identify target populations is critical for effective management of value-based care contracts. Target populations are used to identify high risk patients for enrollment in intervention programs and for constructing risk-adjusted comparison groups to measure outcomes. A one-size-fits-all approach is often insufficient as prediction models may perform well on some populations and not on others. For example, an intervention to reduce readmissions for heart failure patients may not be effective for patients with language barriers, low literacy or those that have unstable housing situations.

Loopback’s Mozart™ population engine powers the learning loop. Data from diverse sources can be used to identify target populations based on risk, cost or gross margin. The tagging architecture allows data from diverse sources to be converted into usable data markers. Mozart can quickly incorporate new and diverse data sources to enhance the identification of target populations and coordinate across multiple prediction models to ensure the most appropriate model is used for each patient.

2. Match

Health Systems often manage many distinct interventions to address different risk factors. The Loopback Platform can leverage available data markers to match target populations to interventions based on patient risk and capacity constraints, cost and benefit characteristics. Higher cost interventions will need to be matched with high risk patients to achieve reasonable return on investment. Machine learning can help identify sub populations that are responsive to specific interventions and those that are not responsive. Patient groups that are not benefiting can be matched to other programs that are a better fit.

3. Engage

Intervention programs may be as simple as notifying a provider to enroll an eligible patient to more complex multi-step workflows that span departments or organizations. The Loopback Platform provides actionable information regarding intervention program performance including program fidelity, productivity of personnel and efficiency. Patients that are eligible for a particular program may choose to opt out or drop out before a program completes. By monitoring the engagement rate for subpopulations, intervention programs can be adjusted to overcome barriers to full participation.

4. Evaluate

The relevant outcomes for target populations and intervention programs will vary based on the organizations priorities and objectives. Common outcomes include capture rates, gross margin, episode costs, utilization (hospitalizations, readmissions, ED visits) and clinical measures such as medication adherence or target lab values. To evaluate intervention programs, baseline measures can be established for each target population. To isolate the impact of an individual intervention, outcomes of an engaged population can be compared to a population with similar characteristics that did not receive the intervention. The data generated from running intervention programs can be utilized to drive continuous improvement.

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