Below are some major ways that Predicting Health Models can be used to improve healthcare:
Predicting Health Models using predictive analysis helps to increase the accuracy of medical diagnosis. This application does not eliminate the role of the doctor in making a diagnosis but rather aids the doctor to make a more accurate diagnosis in a more timely fashion. It helps to provide answers to questions that might have taken hours or even days to answer and it also provides more tools for the doctors to analyze the patients’ condition and arrive at a diagnosis. The predictive analysis tools have access to tons and tons of big data that can all be perused very quickly to find patterns in either that patients medical history or similar medical situations in the past or both to come up with a more detailed and accurate diagnosis. It also means doctors are able to get answers about patients conditions through evidence-based medicine and this will help them determine the exact treatment course to take. This eliminates giving unnecessary treatments or the wrong treatments. It’s usually a wasteful and sometimes deadly problem to patients as receiving the wrong treatment could at worst lead to death or making their condition more severe and at best cost the patient and hospital more money.
With predictive analysis, Predicting Health Models becomes applied in the way of preventive medicine. This means that many diseases can be prevented from even happening by way of identifying at-risk patients and taking proactive steps to prevent them from getting these diseases. For example getting patients to make lifestyle changes that put them in a better position to avoid getting sick with certain diseases. This also leads to better population health and public health. As this continues the focus begins to shift towards good health as opposed to treating already sick patients. Stopping the sickness before it happens and maintaining an overall healthy population. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including the doctor’s and nurses’ time.
Predicting Health Models and Pharmaceutical Companies
One of the uses of predictive analytics is in pharmaceutical companies who use predictive models to over time, develop a medication that is more targeted. Using this predictive analytics is aimed at meeting the medication needs of the public and of course, they, of course, get to benefit financially as well. Bulk medication can be found to not be as effective so with analytics medications can be developed to meet the needs of smaller groups. This will help reduce the unwanted side effects of certain medications. Medication made for larger groups delivery method can expose patients to those risks unnecessarily if the medication is not needed for them. Thus through these patients have access to better care and better outcomes in their treatment and overall health. Patients would be able to receive medication that is guaranteed to work for them and not just medication that’s spread out to larger groups and has a less chance of actually treating them. This would also lead to increased patient engagement as patients would understand that the more informed they are, the more they can engage with their doctor which will lead to more accurate diagnosis and medication prescription. Patients will be more open to using monitoring devices like wearable health devices which will provide their physicians with more accurate information about their health and overall well-being.
Better Population Health
We already discussed briefly the role that predicting health models will play in population health and public health. With predictive analysis, researchers in the healthcare industry will be able to develop predictive health models that will become more and more accurate over time. This improvements no matter how small, play a huge role as small changes can be very statistically significant in the overall improvement of population health. There is a huge difference between statistical difference and clinical difference. It is important that these statistical differences and nuances are properly understood. The population health models get improved on over time and continue to adapt to new cases. This means there will be more accuracy in treating cases and the positive effects of this will be seen in the population over time. It also means systems that have increased transparency and accountability. This is because when it comes to dealing with human life there is no room for mistakes as they could be fatal and lead to dire consequences.
When dealing with human life, the risks of making mistakes are increased, and the models used must lend themselves to making the systems valid, sharable and reliable. Thus the risks of making mistakes are increased, and the models used must lend themselves to making the systems valid, sharable and reliable.