Decreasing Readmissions with Data Analytics

[fa icon="calendar"] Jul 25, 2017 2:49:14 PM


Preventable hospital readmissions are estimated to add $25B¹ to the cost of US healthcare each year and 40-60%² of them can be avoided. Most readmissions are due to a lack of coordinated follow-up care after a patient is discharged from the hospital. Many physicians are often unaware of their patients being discharged or even hospitalized. This is especially true for patients that have multiple chronic conditions, since they see several providers for their care.

Payers have implemented several incentive and penalty programs to ensure that providers reduce their readmission rates. Hospitals tend to use intervention programs to identify at-risk patients, redesign discharge processes, and coordinate follow-up care. Most of these programs involve coordinating care with a large network of providers and services, which makes them difficult to scale and maintain without technology.

Analytical models can be used to predict the probability of a patient’s readmission within 30-days or longer. This data can be integrated with EMR systems and used to design models for appropriate discharge protocols. An analytics platform can effectively integrate this network, measure progress against the care plan and intervene as needed can mean the difference between short-term reductions in preventable readmissions and significant change. This type of platform can support a medical system, reusing the data captured as a byproduct of care to identify what works and what needs to be improved.

Using this model requires technology that can incorporate data about a system, monitor the performance of that system, intervene accordingly, and optimize the process. It must be able to aggregate data throughout the care process and measure the system's progress, as well as integrate with the related network of providers and services. This solution also needs to measure care for both individual patient records and entire populations to initiate post-discharge care coordination and enable appropriate intervention across the entire care team.

To learn how Somnoware helps facilities automate their testing processes, obtain predictive insights, and drive greater patient engagement, please select this link.


Topics: clinical sleep data, sleep health management, sleep center software, sleep center emr, cpap adherence, sleep medicine software, sleep lab emr, sleep lab software

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