This podcast discusses how using medical insurance records can help anticipate future medical needs and reduce healthcare costs. Predictive analytics uses data from medical records, treatment outcomes, and more to predict things like responses to medications, hospital readmission rates, and future health conditions. This can help physicians with diagnosis and reduce costs. For example, predictive models are used to assess when patients can safely be discharged from the hospital to meet regulations. Medical insurance records predictive analytics has the potential to improve outcomes and reduce costs through more informed care, treatment that works for individuals, and awareness of health risks.

1. Dr. Obumneke Amadi-Onuoha_Scripts
The Informatics Podcast Notes: Using medical Insurance Records to Anticipate Future
Medical Needs and Reduce Costs: focus on cost /resources.
This podcast presentation is of a specific health problem that can be addressed by
biomedical informatics methods and techniques. The health care issue presented in the
podcast is: Using medical insurance records to anticipate future medical needs and reduce
health care costs.
I will start by Introducing this Specific Health Problem
Everyone is a patient at some time or another, and we all want good medical care. Health
care costs in the United States exceed 14 percent of the gross domestic product, far more
than in any other nation. Well over 30 million Americans are uninsured, partly because of
rising premium costs. Also, a common challenge in healthcare today is that physicians
have access to massive amounts of data on patients, but little time nor tools. Intelligent
clinical decision support anticipates the information at the point of care that is specific to
the patient and provider needs. However, Electronic health records (EHR), now
commonplace in U.S. healthcare, representing the longitudinal experience of both
patients and doctors. These data are being used with increasing frequency to predict
future events in patient care. In this podcast presentation, I propose an approach to part
of this problem that has been neglected, one that focuses on systematically reducing the
need and thus the demand for medical services
I Propose a Vision for a Biomedical Informatics-Based Solution, thus:
Using bioinformatics process/tools to inform healthcare providers and their management
towards consumer health education e.g. using medical predictive analytic to improve
health care diagnosis and cost attached to care and treatment delivery such as, medical
insurance company records can be used to make accurate predictions about future health
conditions in their members.
Predictive analytics (PA) uses technology and statistical methods to search through big
data of information, analyzing it to predict outcomes for individual patients, data from
past to the present treatment outcomes databases. Predictions can range from responses to
medications to hospital readmission rate, that can be used to helping a physician with
diagnosis, and even predicting future wellness.
In continuation, I want to leverage Some Benefits of PA:
In the United States, providers and practitioners are adopting to some new government
regulations, for example, under the Affordable Care Act, one of the first mandates within
Meaningful Use demands that patients not be readmitted before 30 days of being
dismissed from the hospital, in this case, PA models are used by hospitals to accurately
assess when a patient can safely be released.

2. Predictive analytics can provide employers and hospitals with predictions concerning
insurance product costs. Employers providing healthcare benefits for employees can
input characteristics of their workforce into a predictive analytic algorithm to obtain
predictions of future medical costs. Predictions can be based upon the providers own data
or the provider may work with insurance companies who also have their own databases to
generate the prediction algorithms. Health care providers and hospitals, working with
insurance companies, can coordinate databases and actuarial tables to build models and
subsequent health plans. Healthcare organizations can use predictive analytics to
determine the best and effective providers to obtain products for their particular needs.
predictive analytics can support the Accountable Care Organization primary goal towards
the reduction of costs by treating specific patient populations successfully. As a result,
supply chain management for hospitals and insurance providers will change as needs for
resources change, thus, PA has a way of bringing attention to that which may not have
been seen before.
In addition, Other ways Using medical Insurance Records to Anticipate Future
Medical Needs and Reduce Costs as predictive analytics include:
1) Pharmaceutical companies can use medical Insurance Records predictive
analytics to best meet the needs of the public for medications such as medication
that are found not to help many of those who had prescribed them and predict
those who might benefit from them.
2) Patients have the potential benefit of better outcomes due to predictive analytics,
such that potentially individuals will receive treatments that will work for them,
the patient role will change as patients become more informed consumers who
work with their physicians collaboratively to achieve better outcomes and Patients
and their providers will become aware of possible health risks.
In conclusion
Medical Insurance Records predictive medicine like this has the potential to improve
medical outcome, it could reduce healthcare costs. Also, it could accurately revolutionize
the way medicine is practiced for better health and cost reduction.