How Predictive Analytics is Redefining Personalized Patient Care

Posted by Umang Dayal · 14 Dec, 2023 · 8 min read
How Predictive Analytics is Redefining Personalized Patient Care

We all know doctors are superheroes, but what if they could predict future diseases? No, I am not talking about a new superhero action film, but rather science fiction that’s turning into reality much sooner than you think.

This sci-fi concept is called predictive analysis, which can allow doctors to predict health diseases and prevent them before they even occur. Not only this, but medical experts could even reverse the effects of diabetes and heart attacks. But how exactly will predictive analysis change the future of personalized patient care? Let’s dive into this technological journey!

What is Predictive Analytics?

Your brain can already make future predictions. If you drink something unhealthy and it causes bloating, your mind remembers this and will remind you to avoid it in the future. But when data complexity increases, this prediction becomes difficult for your brain to process. This is where predictive analysis can help; it can analyze a large amount of data, such as family diseases and medical history, read complex cases, and detect health risks before they manifest. This allows doctors to detect medical conditions earlier, make timely interventions, and take preventive measures for the future. Predictive analytics can even help in emergency surgeries or long-term personalized patient care. Overall, it provides effective and timely insights to make acute decisions.

Predictive analytics marks the realization of sci-fi fiction turning into a tangible reality. In the healthcare industry, predictive analytics involves utilizing big data and machine learning algorithms to scrutinize extensive medical data and discern trends and patterns for forecasting future outcomes.

Current Landscape of Predictive Analytics

As per the Statistica report, 59% of healthcare institutions throughout India and 66% in the US have embraced predictive analytics to enhance the delivery of healthcare. The chart below shows the adoption rate of predictive analysis in the healthcare sector for selected countries.

How does it work in the Healthcare Industry?

Doctors and medical practitioners are already utilizing predictive analysis to identify chronic diseases, mitigate the threats of pandemics, and much more.

Mitigating the Threat of COVID-19

Predictive analysis played a vital role in predicting COVID-19 patterns and significantly mitigating its global impact. It has proven instrumental in obtaining data-driven insights, making calculative decisions, allocating resources, and improving personalized patient care.

Numerous hospitals utilized predictive analysis to estimate medical cases, which allowed them to prepare for medical supplies, hospital beds, equipment, and major surgeries.

Parkland Memorial Hospital fought the pandemic by ingeniously adopting predictive analytics as its main weapon against the virus. During the crisis, the hospital could anticipate a surge in COVID cases and employed geographical mapping to find positive cases, an inventory tracker to maintain supplies, and even chatbots to keep families informed about the medical conditions of the patients. It became possible only because Parkland Hospital utilized predictive analytics as its most strategic resource against COVID-19.

Identifying Chronic Diseases and Preventing them

Predictive devices have been successful in identifying patterns in patients' health and notifying them of symptoms even before they manifest. This early detection allows doctors to intervene on a timely basis and recommend patients with personalized medical treatment. In 2016 a study revealed a predictive model that detected Parkinson's disease in a patient and achieved 96% accuracy.

With time, predictive analysis has successfully managed and prevented chronic diseases such as Parkinson's, Alzheimer's, diabetes, and even cancer. It can predict using streams of real-time data and harness this information to deliver effective insights that can later be used to treat diseases.

It assumed a significant role in the prevention and effective management of chronic diseases such as diabetes, cancer, Parkinson's, and Alzheimer’s. These prediction models can read a stream of real-time data, and predictive models can harness this information to derive effective insights into a patient’s health, treatment response, and the progression of their disease. There have been cases where predictive analysis alerted caregivers to deteriorating symptoms. Most recently, one machine learning prediction model successfully prevented a heart attack in a person.

Predictive Analytics Model is Improving no-show MRI Situations

Unattended MRI appointments still present a challenge for healthcare systems to manage, which can lead to patient waiting times and even impact medical costs. These repercussions can lead to delayed diagnoses or exasperated health conditions. Researchers discovered that predictive analysis can offer solutions to reduce outpatient No-Show MRI appointments. These prediction models analyze a diverse set of data, read medical reports, study past appointments, understand patients' demography, factor in external situations like weather and traffic, and consequently identify relatable patterns that lead to no-show MRI appointments.

To manage this problem efficiently, these prediction models follow a proactive approach of constantly reminding patients of their appointments through calls, texts, or emails. To further reduce no-show MRIs, some clinics have even introduced transportation services to encourage patients to attend their appointments.

Also, Read: Virtual Reality (VR) in Healthcare - Applications & Benefits

What Does the Future Hold for Predictive Analytics in Personalized Patient Care?

Prediction model technology is elevating the healthcare industry to unprecedented heights, from identifying individual health risks to projecting the outcomes of specific treatments. Here are some future trends that will shape predictive analytics in personalized healthcare:

Implementing Predictive Analytics in Health Insurance

Predictive analytics can accurately determine the cost of health insurance for each individual based on factors like age, gender, medical history, insurance case records, heredity, and more. Additionally, this technology proves invaluable in thwarting fraudulent insurance claims.

The National Healthcare Anti-Fraud Association reports substantial financial losses ranging from 3% to 10% of healthcare funds (nearing $300 billion) due to healthcare fraud. Leveraging predictive analytics, insurance companies can deploy and train machine learning algorithms to swiftly identify any suspicious intention behind a case, thereby mitigating losses and proactively preventing future fraudulent attempts.

Leveraging Predictive Models to Reduce Hospital Readmissions

In 2018, the readmission rate for adults averaged 14%, out of which 20% of cases were associated with one of four conditions; diabetes, heart failure, COPD, or septicemia. By leveraging socioeconomic data, Electronic Health Records (EHRs), and predictive analytics, it becomes possible to identify patients at high risk of readmission. This provides timely warnings and the implementation of additional medical care at timely interventions, ultimately reducing readmission rates.

Advancements in Predictive Modeling Techniques

The healthcare industry is progressively integrating AI and machine learning technologies to utilize predictive modeling. These prediction technologies can handle vast and intricate datasets, discerning complex patterns, and providing accurate predictions for health conditions. Employing deep learning algorithms and recurrent neural networks can be applied to process diverse healthcare data sets, ranging from medical images and genetic sequences to generate electronic health records.

Overcoming Limitations and Expanding Predictive Capabilities

The accuracy of predictive models within the healthcare industry relies on the availability of high-quality, comprehensive data. It is crucial to improve data collection, standardization, and interoperability across healthcare systems to ensure reliable inputs for predictive models, thereby boosting its effectiveness.

Predictive and Proactive Elderly Patient Care

Many patients face the post-hospitalization challenge of being discharged without continuous health monitoring, leaving them susceptible to avoidable and adverse events. This healthcare gap is particularly risky in elderly patients, where home falls are prevalent and a major contributor to both fatal and nonfatal injuries. Here, predictive analytics presents an opportunity to shift from a reactive healthcare approach to a proactive one.

By amalgamating data from various sources, such as hospital-based electronic medical records, fall detection pendants, and the historical use of medical alert services, predictive analytics can identify senior patients at risk of emergency transport within the next 30 days. This proactive approach allows healthcare providers to reach out to seniors even before a fall or other medical complication, potentially preventing unnecessary hospital readmissions and reducing costs related to transportation, acute care, and rehabilitation.

Harnessing the Power of Big Data and Computer Processing

The combination of big data and predictive analytics will enhance prediction models. It is seeing a rising inclination towards wearable sensors and contemporary health apps. These sensors enable continuous monitoring of patient's health metrics. Predictive models analyze real-time data and identify deviations from standard patterns, allowing timely interventions and proactive healthcare support and management.

Despite promising strides, only 15 percent of hospitals currently utilize advanced predictive analytics. However, there is a strong interest in the healthcare industry to eventually adopt and harness the power of predictive analytics to address both clinical and operational challenges.

Wrapping Up

Predictive analytics empowers doctors and medical practitioners to anticipate potential health risks and identify serious health conditions, enabling timely intervention or avoiding the escalation of diseases. This proactive approach can significantly impact patient outcomes and reduce the burden on the healthcare system.

Prediction treatments have the potential to enhance patient satisfaction, avoid readmissions, collaborate in medical research, and save healthcare organizations valuable time and resources. Although there are ongoing doubts about ensuring the safety and effectiveness of utilizing these technologies, the pandemic has proven that predictive analysis has indispensable potential. The future of healthcare has arrived, and it's up to us to embrace these possibilities.

In conclusion, SoluteLabs stands at the forefront of healthcare innovation, with its patient care software being a testament to its expertise and commitment to enhancing healthcare delivery. As we embrace the future of healthcare, it's clear that SoluteLabs will continue to play a pivotal role in shaping this ever-evolving landscape, ensuring that the best of technology is always at the service of patient care and medical advancement.

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