How Predictive Analytics Impacts the of Healthcare Essay Paper
Describe how predictive analytics could impact and transform healthcare delivery?
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Describe how predictive analytics could impact and transform healthcare delivery.
Predictive analytics and its impact on Healthcare:
Predictive analytics is a type of data analysis that can make predictions about future outcomes using historical and current data. Predictive analytics has had a major impact on healthcare since the last decade, as it can help predict health outcomes and likely events before they occur. Many healthcare facilities, especially hospitals, using predictive analytics have benefited from improved patient satisfaction by receiving interventions planned using predictive analytics that prevented adverse outcomes.
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Predictive algorithms used in predictive analytics can inform caregivers and clinicians about the likelihood of outcomes and events before they occur. This algorithm is processed with real-time and historical data, and artificial intelligence (AI) and the Internet of Things can be used to make predictions about patient outcomes or healthcare events. Using predictive analytics, clinicians can anticipate risk and take action to mitigate it. For example, a patient with pneumonia can be predicted to be at risk for readmission to the ICU within 2 days of discharge, and clinicians can take preventive measures for the patient that can help stabilize their condition without readmission.
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Predictive Analytics has a huge potential for the future also,
Now it assists hospitals in:
Provide anticipatory care for high-risk patients at home to prevent complications: How Predictive Analytics Impacts the of Healthcare Essay Paper
- Identify equipment and its maintenance needs before they arise.
- Predictive analytics can be used in ICUs and wards to predict patient deterioration Due to the emergence of the COVID -19 pandemic, there is a great demand for this type of technology as acute care services surge. Predictive analytics can be used more efficiently for patient care in the future to prevent accidents, poor patient outcomes, hospital infections, accidents, and disasters, etc. with high probability. Predictive analytics can be used in the intensive care unit. Biosensors worn on the patient’s body combined with predictive analytics can enable clinicians to detect signs of complications early. Even without touching the patient, healthcare providers can assess the presence of signs of infectious disease, e.g. COVID -19 clinical monitoring of patients.
- Explanation for step 3
Predictive Analytics can also help in predicting the risk of falls, readmissions, emergency transport to the hospital, complications of COVID-19, degradation of parts in medical equipment or devices ( MRI scanning device ), early warning signs of other technical problems, etc. It can also apply in the areas of efficient patient -scheduling, patient flow, optimal allocation of staff and other resources, etc.
Prognostic models can help maintain patient health by predicting the need for emergency care or the likelihood of a serious condition before it occurs. By identifying patients who are likely to be readmitted to the hospital, predictive analytics can help providers direct additional care where and when it’s needed.
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