Quick Snapshot of our work in the field of Analytics- shot at the Healthcare IT Summit, Mumbai, India. What do we do there with Predictive analytics tools- serious work!!
Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases.
In medicine, predictions can range from responses to medications to hospital readmission rates. Examples are predicting infections from methods of suturing; understanding Patient’s patterns, bias and compliance; determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness.
The statistical methods are called learning models because they can grow in precision with additional cases. There are two major ways in which PA differs from traditional statistics (and from evidence-based medicine):
First, predictions are made for individuals and not for groups
Second PA does not rely upon a normal (bell-shaped) curve.
Prediction modelling uses techniques such as artificial intelligence to create a prediction profile (algorithm) from past individuals. The model is then "deployed" so that a new individual can get a prediction instantly for whatever the need is, whether a bank loan or an accurate diagnosis.
1. Help increase the accuracy of diagnoses.
Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.
2. Helps preventive medicine and public health.
With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid. As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs. With Predictive Analytics, our future medications would be more personalised because predictive analytics methods will be able to sort out what works for people with "similar subtypes and molecular pathways."
3. Provides Physicians with intuitive insights planning treatment methodology for individual patients.
Evidence-based medicine (EBM) is a step in the right direction and provides more help than simple hunches for physicians. However, what works best for the middle of a normal distribution of people may not work best for an individual patient seeking treatment. PA can help doctors decide the exact treatments for those individuals. It is wasteful and potentially dangerous to give treatments that are not needed or that won't work specifically for an individual. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including the doctor's time.
4. Provide employers and providers 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 company's own data or the company may work with insurance providers who also have their own databases in order to generate the prediction algorithms. Companies and hospitals, working with insurance providers, can synchronize databases and actuarial tables to build models and subsequent health plans. Employers might also use predictive analytics to determine which providers may give them the most effective products for their particular needs. Built into the models would be the specific business characteristics. For example, if it is discovered that the average employee visits a primary care physician six times a year, those metrics can be included in the model.
5. Helps researchers develop with even lesser data that can become more accurate over time.
In huge population studies, even very small differences can be "statistically significant." Researchers understand that randomly assigned case control studies are superior to observational studies, but often it is simply not feasible to carry out such a design. From huge observational studies, the small but statistically significant differences are often not clinically significant.
For example, small to moderate alcohol consumption by women can result in higher levels of certain cancers. Many news programs and newspapers loudly and erroneously warned women not to drink even one alcoholic drink per day.
In contrast with predictive analytics, initial models in can be generated with smaller numbers of cases and then the accuracy of such may be improved over time with increased cases.
6. Helps Pharmaceutical companies to plan and best meet the needs of medications for the masses.
There will be incentives for the pharmaceutical industry to develop medications for ever smaller groups. Old medications, dropped because they were not used by the masses, may be brought back because drug companies will find it economically feasible to do so. In other words, previous big bulk medications are certain to be used less if they are found not to help many of those who were prescribed them. Less used medications will be economically lucrative to revive and develop as research is able to predict those who might benefit from them. For example, if 25,000 people need to be treated with a medication "shotgun-style" in order to save 10 people, then much waste has occurred. All medications have unwanted side effects. The shotgun-style delivery method can expose patients to those risks unnecessarily if the medication is not needed for them.
7. Leads to better Patient outcomes.
There will be many benefits in quality of life to patients as the use of predictive analytics increase. Potentially individuals will receive treatments that will work for them, be prescribed medications that work for them and not be given unnecessary medications just because that medication works for the majority of people. The patient role will change as patients become more informed consumers who work with their physicians collaboratively to achieve better outcomes. Patients will become aware of possible personal health risks sooner due to alerts from their genome analysis, from predictive models relayed by their physicians, from the increasing use of apps and medical devices (i.e., wearable devices and monitoring systems), and due to better accuracy of what information is needed for accurate predictions.
Predictive analytics (PA) uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients. That information can include data from past treatment outcomes as well as the latest medical research published in peer-reviewed journals and databases.
In medicine, predictions can range from responses to medications to hospital readmission rates. Examples are predicting infections from methods of suturing; understanding Patient’s patterns, bias and compliance; determining the likelihood of disease, helping a physician with a diagnosis, and even predicting future wellness.
The statistical methods are called learning models because they can grow in precision with additional cases. There are two major ways in which PA differs from traditional statistics (and from evidence-based medicine):
First, predictions are made for individuals and not for groups
Second PA does not rely upon a normal (bell-shaped) curve.
Prediction modelling uses techniques such as artificial intelligence to create a prediction profile (algorithm) from past individuals. The model is then "deployed" so that a new individual can get a prediction instantly for whatever the need is, whether a bank loan or an accurate diagnosis.
1. Help increase the accuracy of diagnoses.
Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.
2. Helps preventive medicine and public health.
With early intervention, many diseases can be prevented or ameliorated. Predictive analytics, particularly within the realm of genomics, will allow primary care physicians to identify at-risk patients within their practice. With that knowledge, patients can make lifestyle changes to avoid. As lifestyles change, population disease patterns may dramatically change with resulting savings in medical costs. With Predictive Analytics, our future medications would be more personalised because predictive analytics methods will be able to sort out what works for people with "similar subtypes and molecular pathways."
3. Provides Physicians with intuitive insights planning treatment methodology for individual patients.
Evidence-based medicine (EBM) is a step in the right direction and provides more help than simple hunches for physicians. However, what works best for the middle of a normal distribution of people may not work best for an individual patient seeking treatment. PA can help doctors decide the exact treatments for those individuals. It is wasteful and potentially dangerous to give treatments that are not needed or that won't work specifically for an individual. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including the doctor's time.
4. Provide employers and providers 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 company's own data or the company may work with insurance providers who also have their own databases in order to generate the prediction algorithms. Companies and hospitals, working with insurance providers, can synchronize databases and actuarial tables to build models and subsequent health plans. Employers might also use predictive analytics to determine which providers may give them the most effective products for their particular needs. Built into the models would be the specific business characteristics. For example, if it is discovered that the average employee visits a primary care physician six times a year, those metrics can be included in the model.
5. Helps researchers develop with even lesser data that can become more accurate over time.
In huge population studies, even very small differences can be "statistically significant." Researchers understand that randomly assigned case control studies are superior to observational studies, but often it is simply not feasible to carry out such a design. From huge observational studies, the small but statistically significant differences are often not clinically significant.
For example, small to moderate alcohol consumption by women can result in higher levels of certain cancers. Many news programs and newspapers loudly and erroneously warned women not to drink even one alcoholic drink per day.
In contrast with predictive analytics, initial models in can be generated with smaller numbers of cases and then the accuracy of such may be improved over time with increased cases.
6. Helps Pharmaceutical companies to plan and best meet the needs of medications for the masses.
There will be incentives for the pharmaceutical industry to develop medications for ever smaller groups. Old medications, dropped because they were not used by the masses, may be brought back because drug companies will find it economically feasible to do so. In other words, previous big bulk medications are certain to be used less if they are found not to help many of those who were prescribed them. Less used medications will be economically lucrative to revive and develop as research is able to predict those who might benefit from them. For example, if 25,000 people need to be treated with a medication "shotgun-style" in order to save 10 people, then much waste has occurred. All medications have unwanted side effects. The shotgun-style delivery method can expose patients to those risks unnecessarily if the medication is not needed for them.
7. Leads to better Patient outcomes.
There will be many benefits in quality of life to patients as the use of predictive analytics increase. Potentially individuals will receive treatments that will work for them, be prescribed medications that work for them and not be given unnecessary medications just because that medication works for the majority of people. The patient role will change as patients become more informed consumers who work with their physicians collaboratively to achieve better outcomes. Patients will become aware of possible personal health risks sooner due to alerts from their genome analysis, from predictive models relayed by their physicians, from the increasing use of apps and medical devices (i.e., wearable devices and monitoring systems), and due to better accuracy of what information is needed for accurate predictions.
