The Role Of Predictive Modeling!
Adel Eldin, MD FACC FACP, MBA, GGA
Board Certified Cardiologist, Founder/CEO Pronto Care and Florida Medical Tourism, Global Healthcare Leader,Entrepreneur, Speaker, Consultant
Predictive modeling is very important in Healthcare Management. Addressing health risks through effective tools can improve patient?outcomes and reduce healthcare costs. Targeted interventions and programs can
?identify useful trends to make informed decisions about patient care. Predictive analytics play a crucial role in precision?medicine, by using lifestyle?and environmental?data to develop personalized treatment plans.
Identifying?individuals?at high risk for heart disease or cancer long before symptoms appear. Thus, improving patient outcomes by changing effective medications along with healthy lifestyle changes.
Predictive analytic data can identify high-risk patients for readmissions. Most of the recommendations for optimal patient management are based on available data from the patient's history, physical exam, risk
?factors, lab results, and other diagnostic tests. This would help focus on implementing preventative treatment?and avoid disease progression thus reducing the chronicity of diseases and the risk of future complications.
Reducing the cost of healthcare by reducing the length of stay. Hospitals can reduce costs and optimize how stocks could be replenished, thus improving the supply chain. This can help hospitals optimize staffing, improve patient satisfaction, and avoid overloading the staff that care for the patient.
Since the readmission rate is considered a common indicator of care quality Medicare will force hospitals to pay penalties?if (readmission to the hospital with the same condition /complaint occurs within 30 days after the initial admission ( for example, recurrent congestive heart failure admission).
It is well known that chronic stress and severe depression are common causes of suicide which can be prevented by early intervention with professional and timely medical support. There are challenges with preventive modeling when it comes to data quality and accuracy as this will lead?to false results.
Standards should be developed with a hybrid model of computer-generated data and human intelligence of integration/analysis, improving and maximizing, not replacing the human element especially when it comes to healthcare delivery, that compassion, and integrity to be combined with trust building to help compliance with lifestyle?changes, medications intake with patient satisfaction will ultimately lead to better healthcare outcome with less overall cost!
Thus, there is a need to integrate predictive analytics which includes empirical methods ( statistical and others) to generate data predictions. Predictive analytics have 6 roles namely, new theory generation, measure development, comparison of competing theories, relevance assessment, improving existing models, and evaluation of predictability accuracy. An example of practical implications is to achieve a tight coupling of financial and operational forecast figures.
Predictive analytics is getting more popular with increasing demand as it can help transform information into meaningful knowledge with high precision accuracy rather than having information overload. Thus, predictive analytics can be used in many fields such as retail, economics, engineering, and insurance not limited to healthcare!