Personalizing Healthcare through Predictive Analytics

Patients, in general, assume that their doctors know everything and that as medical experts, every bit of medical knowledge is stored in their heads. And therefore, as patient assumption goes, diagnoses are accurate and treatment outcomes will be generally positive. However, this is not true. Doctors are not computers and they cannot commit the massive amounts of medical data to their memories. What’s needed is predictive analytics – the next thing in statistical methodologies that will empower patients with individual health profiles, medication and treatment in general.

What is Predictive Analytics?

Predictive Analytics (PA) is a methodology that analyzes massive amounts of information to predict individual patient outcomes. This methodology utilizes technology-enabled statistical methods of information gathering. These vast amounts of information can include the latest medical research outputs, patient’s past treatment history and journals and databases from the medico peer network. Predictive Analytics can help predict patient outcomes and bring surprising data associations to light that doctors would seldom suspect from conventional examinations.

What Is Prediction Modeling?

Medical predictions can be anything from a patient’s diagnosis, prescriptions, hospital readmission, infections, suture methods and the possibility of diseases.Even a patient’s future health can be predicted based on data.Prediction modelling uses artificial intelligence to create an intelligent algorithm based on prediction patterns from previous individuals. This model is then run for new individuals to obtain instant prediction in order to make an accurate diagnosis.

Top Benefits of Predictive Analytics

  1. Increased Diagnostic Accuracy

Availability of predictive algorithms can help in taking proactive steps for measures of diagnostic accuracy. Doctors can add the predictive knowledge to their own assessments and arrive at a quicker diagnosis. Instant and accurate diagnosis will be possible through predictive analytics. With an accurate diagnosis, there will be no need to try out various treatments in complicated cases, thereby compromising patient’s health even further.

  1. Generating Preventive Medication

Several diseases can be prevented or managed much better if early intervention occurs. Predictive analytics can enable physicians to identify at-risk patients, and therefore bolster said patients to take preventive care such as making lifestyle changes. In fact, preventive medication can be generated in the future as predictive analytics methods identify people of similar subtypes based on their molecular tendencies.

  1. Generating Personalized Medication

With predictive analytics, pharmaceutical companies will feel incentivized to develop medications for smaller groups. Several medications have been dropped over time as they are not used by the masses. Now, with predictive analytics, drug companies may feel it economically feasible to develop medications personalized to each group’s needs and the individuals who fall within that group.

  1. Better Personalized Treatment Outcomes

As the use of predictive analytics increases, individuals will receive the treatments that will respond to their conditions. They will be prescribed the treatment that is bound to work for them, individually, and not be burdened with medications that may not be needed for them. Sometimes medicines are prescribed generally because they’ve worked for the majority. They may not work for the individual, but doctors do end up giving out prescriptions for several items, all required or not. This is just a precautionary measure. With predictive analytics, it will be possible to see enlightened patients as they become informed consumers, not just patients at the receiving end. Patients can work collaboratively with their doctors to ensure better treatment outcomes for themselves.

  1. Personalized Health Risk Profiles

Predictive Analytics- he Future of Personalised Healthcare Sector

Patients can be updated on personal health risks based on alerts from genome analysis, and personal health monitoring devices such as heart monitors and other devices. All this plus patient’s medical history is used by Artificial Intelligence to generate accurate health risk profiles that are tailored to the individual. For example, if obesity is a marker for heart disease, it may not be true that all obese people are due for heart disease. Predictive analytics finds a way to let individuals know their individual health risk profiles, despite popular indications.

Conclusion

Changes are happening in the medical world, and predictive analytics is the incoming giant, a statistical revolution that will change the roles of patient, doctor and pharmaceutical companies. Being better informed and being updated on their own individual profiles will help patients assume greater responsibility for their health. Medical professionals will be consultants more than decision makers.