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Healthcare improvement through predictive analysis


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Satish Para, Senior Vice President-Advantages and challenges of predictive analytics in digital solutions, Indium Software, and healthcare that relies on access to secure, high-quality data

The Internet of Things (IoT) and healthcare-grade wearable devices have revolutionized healthcare by giving users more control over their health. Healthcare service providers can also provide better and more timely service to patients by remotely monitoring their health parameters and providing on-time treatment to improve their outcomes.

The big data generated by these devices also serves as a treasure trove of training data that can be used to understand disease patterns using statistical tools for predictive modeling focused on treating and improving treatment of patients. To do. Of course, this must be done in compliance with patient-specific data and privacy regulations.

Predictive analytics integrates machine learning and business intelligence to predict future events from historical and real-time data, potentially becoming a major growth driver for the healthcare industry. According to Marketwatch.com, the global Healthcare Predictive Analytics market is expected to grow from $ 2,439 million to $ 10,740 million by the end of 2026, with a CAGR of 23.4% between 2021 and 2026. I will.

Benefits of predictive analysis in the healthcare industry

Leverage big data to increase the efficiency of the healthcare industry, improve customer service, provide better care, anticipate surges in illness trends, meet greater demand for care, and prepare for future illnesses You can improve innovation by addressing it.

HealthTech companies are also gaining access to venture capital and private equity to develop apps and device products using predictive analytics. These apps use real-time data to alert users and healthcare professionals to imminent danger and access timely care.

But the role of predictive analysis goes beyond patient care. In fact, predictive analysis and intelligence are very useful for operations and management. Here are some of the key areas where healthcare can benefit from using predictive analytics:

●● Operational management

●● Demand forecast

●● Patient scheduling

●● Revenue cycle management

●● Workforce planning and scheduling

●● Corporate finance and financial planning

●● Fraud detection

●● Patient involvement

●● Analysis and management of clinical outcomes

Here are some ways predictive analytics can benefit the healthcare industry:

●● Finding a cure: Prognosis analysis helps physicians find a cure for a particular disease by allowing accurate modeling of an individual’s mortality rate. Companies use data and intelligence to propose customized treatments.

●● Best Treatment Recommendations: Even in healthcare, there is no one-size-fits-all treatment. By analyzing large datasets, physicians can identify correlations and hidden patterns between body shape and prognosis to provide optimal treatment.

●● Preventive medicine: Predictive analysis creates risk scores based on patient health parameters, biometric data, test results, etc., and provides appropriate treatment to slow the progression of individuals vulnerable to chronic conditions early. Helps identify.

●● Dealing with pandemics: Infectious disease epidemics and timely interventions to prevent or at least mitigate the epidemics using predictive models.

●● Preventing Patient Exacerbation: During inpatient treatment, patients are more susceptible to infections and other complications. Predictive analysis helps identify potential risks and mitigate them early.

●● Supply Chain Management: Hospitals can improve spending decisions and purchasing patterns through aggressive supply chain management and cost savings through experience. You can also improve the price negotiation function, ordering process and reduce supply variability.

●● Fraud Detection: Machine learning and business intelligence can help you analyze billing records and patient data to identify anomalies, prevent fraud, and use resources more productively.

Challenges posed by predictive analysis

Undoubtedly, predictive analysis can open the door to some improvements that benefit the healthcare industry and its users. You can provide treatment, improve results, reduce costs, improve efficiency, and more. But tools alone are not enough to achieve this. Here are some factors to keep in mind:

Access to data

The data is the breakwater that forms the basis of predictive analysis. Therefore, access to high-quality, clean data from a variety of internal and external features determines the effectiveness of predictive analysis efforts. To gain meaningful insights, it is important to store data from different sources in different formats in central repositories for rapid integration.

Cloud and on-premises

Security concerns and legacy systems require companies to consider hybrid clouds, creating the challenge of predictive analytics tools working with databases in multiple environments.

Governance and security

Being a highly regulated industry, healthcare players need to ensure the security and privacy of freely available data throughout their life cycle.

Keep up with the times

Technology is changing at the speed of thinking. Maintaining a pace is essential, but it is not only costly, but can also pose other challenges if not well thought out, planned, and implemented. Ensure the scalability of your technology investment and stay adaptable while protecting your investment.

Predict a healthy future

The ultimate goal of all healthcare service providers is to improve treatment outcomes, provide patients with the best possible treatment, and keep operating costs low while preventing disease progression. Predictive analysis can do this if you have access to secure, high-quality data.

Healthcare improvement through predictive analysis

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