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AMxIntelligence

Forecast and understand risk across procedures, therapies and treatments regardless of how complex and massive your data is. Make sense of complex clinical data and patient-reported outcomes before you report out.

Dig deep and identify opportunity with interactive dashboards.

  • Analyze at the patient, physician or site level—either individually or in aggregate—regions, hospitals, physician groups
  • Understand low hanging targets for improvement—drill down into numerators and denominators of measures
  • Explore RWE when and how you need it – create patient cohorts and analyze granular clinical details
  • Access all data elements via an easy-to-use query tool and customized extracts of raw and analyzed data
  • Create patient cohorts, analyze case level details, and draw correlations within and across diagnoses
  • Apply custom benchmarking, peer groups and patient cohorts for fair comparisons

Make more informed decisions and anticipate what is likely to happen next to intervene preemptively.

Enable stakeholders to review and interact with the data to peer deeper into trends and outcomes and forecast negative outcomes—with high probability—so that necessary actions to provide the best possible care for individual patients can be taken.

Level the playing field—develop trust in data through risk and reliability adjustments.

Adjust risk levels: account for the severity of a patient’s illness and ensure comparisons of hospitals and clinicians are fair and accurate. Scientific and clinically validated statistical models explicitly evaluate and compare all patients with the same condition but different health statuses based on outcomes: complications, utilizations, mortality

Isolate signals and reduce noise: ensure parity across large and small data sets with reliability adjustment—a statistical hierarchical model pioneered by ArborMetrix. This model solves the problem of too small of sample sizes—not enough actionable clinical data to effectively inform improvement efforts. For example, are outcomes such as mortality due to chance or to true differences in quality? Now clinicians and hospitals can identify real problem areas and focus improvement activities where the greatest opportunity exists.