Reducing Hospital Readmissions with Predictive Analytics

One of the major problems for the healthcare industry as well as individual providers is 30day readmissions. On average, readmissions to any hospital within 30 days lead up to losses of about 26 billion dollars annually. Reducing avoidable readmissions is a key goal for the Centers for Medicare & Medicaid Services (CMS) while ensuring better quality of medical services and reducing associated financial loss.

Under these circumstances, it becomes essential to find an appropriate solution to avoid unnecessary losses attributed to 30-day readmissions. To prevent providers from bearing penalties and associated waste, predictive intelligence becomes essential. An appropriate predictive tool can help providers efficiently manage the health of patients and simultaneously improve bottom-line.

The Need for Predictive Analytics

With the introduction of HRRP (Hospital Readmissions Reduction Program), there is a demand in the healthcare industry for an ideal solution to reduce financial loss and prevent readmissions. According to the HRRP, providers that have higher readmission rates will have penalties imposed. This program currently aims at the following conditions:

· Heart failure (HF)

· Acute level of myocardial infraction (AMI)

· Pneumonia (PN)

· Total hip arthro plasty (THA) and total knee arthro plasty (TKA)


Predicting Avoidable Readmissions

Several hospital readmission management solutions based on predictive intelligence are available in the market. Providers need to find the best solution that matches with their individual needs to prevent the occurrence of 30-day readmissions. An ideal predictive model works on the following grounds:

· Data gathered from healthcare facilities becomes the base for the creation of specific prediction model

· Continuous update are made to the risk score throughout the patient stay

· Care providers use the risk scores to focus on appropriate measures of individual patient care

Strategies for Preventing Readmissions

Hospitals and private service providers should make use of predictive analytics to reduce their losses. Additionally, certain crucial strategies could be implemented to reduce readmissions rates. These strategies may include:

· Maintaining partnerships with the community of physician groups and healthcare facilities

· Having nurses take over the responsibility of medical reconciliation

· Making arrangements for follow-up visits prior to the discharge of the patients

· Maintaining a process of transmitting discharge summaries to the patients’ primary care physicians

· Assigning follow-ups relating to lab results after the patient discharge

All these strategies can efficiently reduce the chances of readmissions thereby reducing the amount of losses.

Summary: A well-defined predictive model offers accurate solutions for preventing hospital readmissions. It is effective in reducing the amount of losses hospitals bear every year due to conditions including heart failure.