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Improving care with analytics

May 13, 2016
From the May 2016 issue of HealthCare Business News magazine

Public quality data, such as the CMS websites for Nursing Home Compare and Home Health Compare, are too blunt of an instrument to guide network selection. Hospitals need to develop their own ability to evaluate cost and quality performance of network partners, and where necessary, narrow their networks to those providers that are able to meet performance criteria. CMS and other payers are beginning to make claims data available for patients in risk-sharing reimbursement models.

Whether under bundled payments, ACO or managed care models, hospitals must be ready to leverage claims data to develop standardized, risk-adjusted measures to inform post-acute care partner selection and performance. On an on-going basis, claims analytics can help pinpoint sources of variation in cost and quality to enable the development of effective intervention strategies and continuous improvement programs.

Network management
While claims analytics can provide a big picture overview of performance, claims data typically lags by several months. It’s difficult to steer process improvement using only a rear view mirror. To effectively manage patients across a network of providers, real-time analytics are also needed. Hospitals will need data sharing agreements with post-acute care network partners, and an ability to integrate data from multiple systems.

With data sourced from different organizations, a Master Person Index (MPI) will be needed to link patient and provider encounters across systems, and care settings to construct a longitudinal view. Once real-time data has been matched up, more comprehensive analytics can be developed that leverage data from network partners. Real-time patient tracking can be linked with costs from historical claims data to establish predictive cost models that help network providers anticipate episode costs and preemptively intervene with high-risk patients.

Network interventions
As patients move through the care continuum, data markers need to be continuously monitored in real time to identify high-risk patients that need more intensive care. Predictive analytics can identify patients of rising risk to match with appropriate interventions. With an effective feedback loop, advanced machine learning algorithms can optimize risk models over time to improve accuracy as hospitals expand their access to new data sources and gain experience.

About the author: Neil Smiley founded Loopback Analytics in 2009 to deliver an advanced Software-as-a-Service platform health care providers can use to prevent costly readmissions. The Loopback Analytics team currently works with the largest pharmacy, hospitalist group, health system, payer, and senior housing provider in the nation, providing proven intervention solutions that improve clinical outcomes and reduce the total cost of care.

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