For any healthcare organization, patient readmissions present a major challenge. Currently, one in every five patients who are discharged from the hospital is readmitted in less than 30 days. Hospital readmissions are expensive and more often than not they are avoidable, but avoiding them is still a major challenge for healthcare organizations. Also, the legislation penalizes healthcare organizations with comparatively higher readmission rates, so reducing the readmission rate becomes a necessity.
While the reasons behind readmissions of patient are manifold, they are the outcome of inadequate follow-up care or poor discharge procedures. Big data analytics can be taken into account to eliminate unnecessary readmissions that can be evaded by proper post-discharge care. Machine learning can provide clinicians with daily updates on patients’ status, predict which patients are more likely to need readmission, and how they might be able to reduce the risk of readmission.
Using a machine learning platform, healthcare organizations can develop a good patient monitoring plan to anticipate the potential time of readmission and to detect any conditions or symptoms that may cause the readmission. It allows hospitals to find the reasons behind high readmission rates and address them by coordinating with care providers and physicians to optimize hospital stays.
By making use of clinical and social data, present as well as historical, hospitals can identify high-risk patients, treat them before they become critical and reduce unnecessary admissions and improve patient care. Combining clinical, operational and financial data together can help in identifying the treatments and programs that are not delivering desirable outputs or are too expensive to operate. An ideal patient monitoring plan and an optimal readmission prediction will help in mitigating readmissions before they cause an emergency readmission.