Dr. Ali Tinazli is the CEO of lifespin.health and has 15+ years in Fortune 100 corporate strategy and entrepreneurship (SONY, HP).
Without question, the U.S. is home to some of the best medical facilities in the world. However, the current healthcare system that supports these hospitals is complicated and riddled with obstacles.
Complex and slow-to-change healthcare policies exacerbate critical issues like rising costs and overcrowded conditions. Add to this, post-Covid-19 burnout has physicians and nurses leaving the medical field in droves.
Fortunately, technology has cost-effective solutions to many problems plaguing the U.S. healthcare system today. Machine learning, artificial intelligence and big data can deliver robust and innovative answers to age-old problems.
One technological advancement that holds tremendous promise for the future of healthcare is predictive analytics. It has the potential to significantly transform not only the U.S. healthcare system but even the global system by providing powerful tools for identifying and addressing health risks, improving patient outcomes and reducing healthcare costs.
By analyzing large amounts of data from various sources, predictive analytics can identify patterns and trends that can inform the development of targeted interventions and programs and help healthcare providers make more informed decisions about patient care. Additionally, the technology can help physicians develop personalized treatment plans for individual patients, which can help to improve outcomes and reduce healthcare costs.
The Role Of Predictive Analytics In Precision Medicine
Predictive analytics plays a crucial role in precision medicine, an approach to healthcare that uses an individual’s genetic, lifestyle and environmental data to develop personalized treatment plans.
One of the critical advantages of precision medicine is that it allows healthcare providers to identify individuals at high risk for specific conditions and diseases, such as cancer or heart disease, long before symptoms appear. By identifying these individuals early, healthcare providers can take proactive steps to prevent the progression of the condition or disease, leading to better patient outcomes.
Another advantage of precision medicine is that it allows healthcare providers to tailor treatments to each patient’s specific needs based on their unique characteristics. For example, by analyzing an individual’s genetic data, healthcare providers can identify which medications are most likely to be effective for that patient and which may cause side effects.
By analyzing an individual’s lifestyle and environmental data, physicians can identify risk factors and make recommendations for lifestyle changes that can help to prevent the development of certain conditions or diseases.
Predictive Analytics’ Broad Implications For The Future Of Healthcare
A branch of data analytics, predictive analytics uses statistical techniques, machine learning algorithms and data mining to analyze historical data and accurately predict future events or outcomes. The technology involves many processes—collecting and cleaning data, data exploration and visualization, feature selection, model building, evaluation and deployment.
The data used in predictive analytics for healthcare can come from various sources, such as electronic health records (EHRs), claims data and wearable devices. Rapid and highly efficient data collection will radically transform how healthcare providers process complex information.
Analyzing Genomic Data To Better Predict Patient Outcomes
We can now analyze genomic data to predict patient outcomes, such as the likelihood of a patient responding positively to a particular treatment. Predictive analysis allows physicians to focus only on crucial and relevant data sets when formulating precise patient treatment plans.
Furthermore, the technology can support personalized medicine by identifying the genetic factors that make individuals more susceptible to certain conditions and identifying the specific genetic markers that drugs can target.
In the future, lab results on genomic data should be as easy to acquire as any other commonly run blood test.
Targeted Interventions To Reduce Patient Readmissions
From March 13 to April 9, 2020, 4.5% of hospitalized patients with Covid-19 at New York‐Presbyterian Queens experienced an unfortunate boomerang effect: returning even sicker for readmission within 30 days of discharge. Of these patients, 1 in 5 of the Covid-19 survivors died upon readmission.
Predictive analytics can identify individuals at risk of hospital readmissions and help providers develop targeted interventions to reduce the likelihood of repeat stays.
While the technology is still in its infancy, rapid and accurate AI-driven data delivery could be highly beneficial in assessing patients’ discharge readiness on a broad scale for future global and personal health emergencies.
Solving Today’s Dilemmas With Next-Generation Technology
Predictive analytics will significantly impact precision medicine. The technology can rapidly and accurately identify individuals at high risk for certain conditions and tailor interventions to specific individuals based on their unique characteristics.
Accessing vital information from genomic data using machine learning algorithms to analyze the large amount of data generated by genome sequencing can be a powerful tool for overwhelmed physicians in clinical settings. AI may mitigate physician burnout by making their work easier with rapid access to accurate data.
Technology can vastly improve how hospitals and clinics function globally, improving the flow of data and knowledge transfer—and, ultimately, improving the cost pressure. Perhaps most importantly, it will strengthen physician-patient collaborative efforts, fostering trust, greater transparency and affordability for the future of healthcare.
Challenges And Limitations Of Predictive Analytics
Predictive analytics has the potential to revolutionize healthcare by providing insights and predictions that can inform decision-making and improve patient outcomes. However, we must address the challenges in using the technology to ensure reliable and trustworthy results.
Addressing the limitations of predictive analytics in healthcare requires a multifaceted approach that involves careful data preparation, model selection and performance evaluation, as well as a strong focus on privacy, security and fairness.
While this technology holds great promise in healthcare, it is crucial to understand and address the inherent challenges and limitations that arise in its application. From data quality and model selection to overfitting, algorithm bias and explainability, each of these challenges requires careful consideration and a thorough understanding of the underlying methods and techniques.
Most importantly, the complex and dynamic nature of healthcare data requires constant monitoring and adaptation. Predictive analytics brings tremendous promise for meaningful and actionable insights in the advancement of modern medicine.