Predictive Analytics is the branch of advanced analytics that uses techniques like data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data and make predictions for the future. It has got implications in various industries and plays a significant role in the healthcare industry as well. Right from helping inaccurate diagnosis, it can assist in preventive medicines and public health, predictions about insurance costs, research on medicines, and a better outcome for the patients. This blog will focus on how predictive analytics based on electronic medical records can benefit patients and doctors.
The Common Scenario
Everyone is a patient at one or the other time and need medical care. It is assumed that doctors are experts and provide the best research-based solutions for any regular or emergency situation. No doubt, doctors give their best, but it is practically not possible for them to memory all the knowledge and treatment outcomes for various situations and retrieve them instantly on their fingertips. It is in this situation, that analysis of data is needed from patient’s past history, family history, past treatments, diagnosis, medications, and results for a better treatment action in the current situation.
Diagnosis and Treatment Without Electronic Medical Records
The use of conventional methods to diagnose and treat patients comes with its own set of difficulties some of which are listed below.
- The patient’s ability to recall and explain the problem in a way that doctors can comprehend.
- The patient’s ability to memorize and explain everything at a time for better correlation by the doctor.
- The documentation of all the past prescriptions and diagnostic reports to produce before the doctor.
- Doctor’s ability to refer and correlate all the hard copies of reports at the time in emergencies.
With an increasing number of diseases and patients, the demand for doctors is soaring. The per-patient time allocated by doctors needs to be economized to accommodate the number of patients without compromising on the quality of service delivered. Here, an EMR can play a significant role by presenting a patient’s data or personal health records to the doctor in a format which is easily comprehensible and accurate.
How Predictive Analysis of EHR can help doctors
- Improved Care Coordination: Sharing of accurate information at a time among the providers leads to improved coordination and action. They can analyze the data and conclusions based on commonly shared information.
- Improved diagnostics & patient outcomes: The access to information about past medication, allergies, family history, and response to various treatments help doctors to predict the patient risk score and plan a treatment that is apt for him. Knowledge of past history eliminates the trial of treatment regimens which may or may not work for a given patient and allows the implementation of a treatment plan which is most likely suitable for the patient.
- Practice Efficiency: Electronic prescriptions, quick data retrieval of lab results, and easy documentation have made the practices more efficient. With patients’ data in hand, doctors are quick in responding and acting on revisits.
How predictive analysis of EHR can help patients
- Improved diagnosis and accurate treatment: Treatment plan based on analysis of past records eliminates errors and yield better results by coming up with the right diagnosis and appropriate treatment.
- Predictability of diseases help in preventive care: Analysis of family history helps in the prediction of some obvious health conditions which helps in taking preventive treatment on time to avoid serious diseases.
- Suitability of treatment plan can be predicted: Analysis of medicine performance, allergies, and procedures help in choosing the right treatment in the very first instance.
“Predictive Analytics and Electronic Medical Records are re-engineering the pathway for personalized care”
Predictive analytics has changed the way doctors have been doing it all the way for many years by measuring, aggregating, and making sense of hard-to-obtain behavioral, psychological, and biometric data. The combination of these new datasets along with the clinical medicines and innovations has resulted in the re-engineering of clinical pathways and truly personalized care for the patients for better outcomes.