The benefits of using predictive analytics in healthcare
One of the emerging technologies that is making a significant impact is predictive analytics, enabling healthcare providers to shift from reactive to proactive care delivery.
Predictive analytics refers to examining data and applying statistical models to identify patterns and trends to predict future outcomes. Healthcare can apply this approach to a wide range of areas. This includes identifying patients at risk of developing a particular condition and predicting treatment effectiveness.
Here are six benefits of the application of predictive analytics in the healthcare sector.
- Chronic disease management
- Proactive care
- Identification and management of at-risk patient cohorts
- Targeted treatment plan
- Improved hospital administration and operational efficiency
- Fraud monitoring and prevention
In the United States, chronic illnesses are the leading contributors to mortality and impairment. Chronic disorders such as cancer, cardiovascular disease, diabetes, obesity, and kidney disease account for 75% of healthcare spending.
When assessing potential risk factors for chronic diseases, it is imperative to consider the patient's and their family's medical history, lifestyle choices, eating habits, levels of physical activity, etc. A robust predictive model can quantify a patient's risk by accounting for all these factors and recommending treatment (exercise, nutrition, and medication) for preventative care.
Predictive analytics empowers healthcare practitioners to proactively detect biological and socioeconomic risk factors, allowing for early intervention and prevention of chronic conditions.
Predictive analysis has proven effective in identifying clinical deterioration at an early stage, as well as in monitoring surgical patients for septic shock and haemorrhage. This serves as an early warning system, allowing healthcare providers to intervene promptly and prevent potentially negative outcomes.
As a result, healthcare is moving towards a new era of proactive care models that rely on predictive analysis to identify patterns in patient data, enabling clinicians to anticipate and address potential risks before they become critical. This approach replaces reactive care models and leads to better quality care delivery at a reduced cost, benefiting patients, providers, and payers alike.
Predictive analytics can be useful not only for individuals but also for managing the health of entire populations. By collecting and analysing data on personal medical history, current health conditions, and medications, it's possible to identify other patients with similar profiles within a population group. With the help of machine learning algorithms, healthcare professionals can study past patient procedures and identify the most effective clinical pathways for achieving positive patient outcomes. This allows healthcare providers to promptly begin treatment, thereby increasing the chances of survival for patients.
Previously, the medical field relied on a general approach where treatments and medications were prescribed based on limited information about large groups of people. However, with the availability of extensive healthcare and personal data and Big-Data analysis, there is now an opportunity for a more personalised approach to healthcare. Medicine can be tailored to meet the needs of each individual, resulting in safer and more effective treatments.
For example, the traditional approach to treating high blood pressure (BP) is to prescribe medication that works for most people but not necessarily for the individual. With the personalised approach, the patient's genetic makeup is factored in so that the prescribed medicine is more effective for that individual. This can lead to better outcomes and quality of life for the patient.
Predictive analytics can benefit the administrative side of healthcare by preventing downtime due to scheduling issues or delays in claims processing. This can optimise workflow processes, making them more efficient and streamlined.
Predictive analytics can help predict patient traffic, which can help schedule appointments at the optimal capacity. Additionally, it allows hospitals, insurance companies, and patients to collaborate more efficiently to process claims.
Moreover, automating tedious tasks with predictive analytics can create a stress-free work environment for healthcare personnel, enabling them to focus on providing better and more efficient customer service.
In conclusion, predictive analytics enables healthcare providers to keep patients in-network and enhances operational efficiency.
Unfortunately, healthcare fraud is a prevalent issue that involves various fraudulent activities, including upcoding, billing for unnecessary services, falsifying claims, and kickback schemes.
Upcoding involves billing for more expensive services or procedures than were provided. In contrast, billing for medically unnecessary services or services not performed violates federal healthcare regulations.
Kickback schemes involve offering bribes to healthcare providers in exchange for referrals, prescriptions for a specific medication, or the use of particular medical devices.
Falsifying claims often entails identity theft, with unlicensed medical personnel performing services and misusing licensed physicians' signatures.
Predictive analytics combats fraud in healthcare by identifying patterns and flagging abnormalities. This way, it can detect fraudulent activities early on and avoid losses.
Predictive analytics can revolutionise healthcare by enabling more personalised, efficient, cost-effective and effective care. By harnessing data and machine learning, healthcare providers can identify patients at risk of chronic conditions. They can optimise treatment plans, reduce readmissions, and improve patient outcomes.
With the continued development of new technologies and approaches, predictive analytics is poised to play an increasingly significant role in healthcare's future.
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