How to predict Patient UXP in the Healthcare sector with AI-driven decision-making for better optimal care

 

Predictive Analytics in Healthcare

Predictive Analytics: Predictive analytics is a part of advanced analytics in Artificial Intelligence that makes predictions about future results or forecast activity, behavior, and trends. With the use of historical and current data combined with data mining techniques, machine learning (ML), statistical modeling and superior AI/MLOps algorithm, we make faster decisions. Organizations or companies apply predictive analytics to find patterns in the data to identify risks and opportunities. With predictive analytics with the help of Data Science and Big Data, the healthcare sector can predict patient journeys with better solutions. In today’s world organizations and companies have to deal with enormous data, which is, received through different sources in the structured and unstructured form of images, video, sensors, transactional databases, equipment log files, and other data sources. To get meaningful insights from this data, statistical techniques in predictive analytics are used, including Data Modeling, Machine Learning (ML) Artificial Intelligence (AI), Deep Learning Algorithms, and Data Mining, therefore, predictive analytics can be applied to past, present, or future. Predictive analytics tools have become sophisticated enough to retrieve meaningful insights for better decision-making. 

There are different types of models:

1. Predictive Models 

2. Descriptive Models and, 

3. Decision Models and is used in other sectors like Banking, Retail, Supply Chain, Marketing and Sales, Human Resources (HR), and Healthcare. 

Predictive analytics in healthcare is used for risk-scoring for chronically ill patients to detect the early signs of patient deterioration. FutureAnalytica solution for Predictive analytics in the healthcare sector your organization can accumulate a large amount of patient data from Electronic Health Records (EHR), administrative paperwork, insurance claims, medical imaging, etc., and process it to check for different patterns. Through FutureAnalytica AI-backed data-led analysis, you can map out the patient journey.

1- What type of diseases patients is likely to develop?

2- How patients might respond to a different style of treatment?

3- Will they return to the hospital after discharge?

Our FutureAnalytica predictive analytics in healthcare, offers clinical decision support, diagnosis mapping, treatment course design, adverse events reduction, remote monitoring, care quality improvement, and overall cost reduction. 

Why is Predictive Analytics important in Healthcare?

With advanced technology being recognized as an integral part of healthcare service delivery, it stands to be a key beneficiary of predictive analytics. The progress towards the adoption of technology in the healthcare sector which is undergoing drastic change has a big-time positive impact on medical processes and practices in which healthcare professionals engage. Some of the key milestones include the digitization of health records, advanced software, access to big data, storage in the cloud, and mobile application technology. These milestones have presented various benefits in the healthcare sector, including faster access to information, lower healthcare costs, ease of workflow, the overall improvement of quality of life, and improved public health.

They have also assisted in the development of new drugs and treatments, helping to avoid preventable deaths and significantly reducing health care wastage. Technology will continue to play an important role in improving the health of people and predictive analytics will play an innermost role in this. It helps in an overall improvement in business operations, to enhance the accuracy of diagnosis and treatment of personalized medicine to be assisted and to access potential risk factors for public health.

It needs cohort treatment and epidemiology, possible control of diseases. Predictive analytics is quickly becoming a foundation of personalized healthcare. Using Artificial Intelligence (AI) and Machine learning (ML), predictive models can intake an enormous different amount of data for a patient and forecast the risk of developing a specific disease or predict a given condition. Personalized healthcare can be customized through a patient’s medical history, social risk factors, genetics, environment, and other characteristics. The solution for personalized healthcare is treating a patient based on their specific feature.  

Predictive Analytics in Healthcare examples

The Healthcare industry is transforming with new technology and with the adoption of advanced automation tools which are widely available. Artificial Intelligence analysis in healthcare enables more efficient, real-time decision making, and accurate predictions. Predictive analytics is changing the medical field in different ways, a few examples are given below.

Reducing Hospital Readmission Rates: 

Healthcare predictive analytics can identify patients with diabetes, heart failure, Chronic Obstructive Pulmonary Disease (COPD), and blood poisoning by bacteria - septicemia. With the use of EHRs, socioeconomic data, and predictive analytics, patients with a high risk of readmission can be discovered and allocated additional resources for follow-ups to reduce readmission rates. A Predictive model is built to assign a readmission risk score to every single patient. The Predictive analytics tool predicts patients' symptoms that would appear in the future, thus helping the physician to change the medication, thereby preventing readmission.

Research into New Treatment: 

Predictive analytics can be used effectively in the field of research of new treatments too. Based on clinical history, genetic information, and, other data, predictive models or superior AI algorithms can accurately predict the patient’s response to a medication or suggest a treatment plan. This can streamline the research process and cut down the unnecessary in-patients.

Health Insurance: 

Another use of predictive analytics in healthcare is by speeding up insurance claims which are submitted. The tools can minimize errors while helping hospitals prepare insurance claims faster and can calculate the accurate cost of health insurance for each, specific individual based on gender, age, heredity, medical history, insurance case history, etc. It can also be used for preventing fraudulent insurance claims.

Benefits of Predictive Analytics in Healthcare

There are various advantages of implementing predictive analytics in the healthcare sector using artificial intelligence, machine learning tools, and techniques. It improves business efficiency and assists doctors in providing healthcare services to every individual patient.

Population Health Management: This is another important healthcare predictive analytics example that covers three aspects.

Spotting Chronic Diseases - Predictive analytics in healthcare helps medical institutions to identify people with the risk of developing chronic conditions and provide them preventive care before the disease progresses. This type of analytics assigns records to patients based on a variety of factors, including age, disabilities, past patterns of care, and demographics

Public Health Trend Identification - Predictive analytics additionally in the healthcare industry helps to identify potential population health trends by uncovering the health trends which are harnessing a large population and providing insights to take a real-time decision

Detecting Disease Outbreaks - Predictive analytics in healthcare helps authorities and ordinary people to have an update on the pandemic. A predictive analytics-based tool is developed for COVID-19 tracking, that shows current and future trends of the virus

Enhancing Cybersecurity: Predictive analytics in healthcare can positively contribute to Cybersecurity. Healthcare organizations can use predictive analytics combined with artificial intelligence solutions to calculate risk for different online transactions in real-time and respond accordingly. Furthermore, the predictive models in healthcare can monitor data access and identify any changes in patterns, which could indicate an intervention

Equipment Maintenance: Predictive analytics in healthcare can help you predict when a component needs replacing or maintenance. The healthcare industry involves the usage of several clinical and medical equipment and devices such as MRI scanners that deteriorate over time through regular use. Unplanned equipment maintenance will lead to unplanned downtime and that might even pose a serious threat to a patient’s life. When the equipment is not in use it can be scheduled for maintenance. Thus, minimizing downtime and other unscheduled workflow disruptions

Detecting early signs of Patient Deterioration: Predictive analytics can be an irreplaceable process in the Intensive Care Unit (ICU), and other wards where a patient’s life might depend on timely intervention and a quick reaction time when their condition is about to get worse. As the vital signs of patients are continuously analyzed and monitored, predictive analytics powered by Machine Learning (ML) algorithms and Artificial Intelligence (AI) can help identify patients requiring immediate medical attention. Thus, it triggers an appropriate and early response, when ICUs and other wards are overflowing with patients. Predictive analytics in healthcare can help caregivers in rapid real-time decision-making

Managing Supply Chain: The supply chain represents one of the most significant opportunities for healthcare to cut down unnecessary spending and improve efficiency. Predictive tools can be used for monitoring the supply chain and making proactive, data-driven decisions to get more actionable insights into ordering patterns and supply utilization. Predictive analytics can reduce the variation in supplies, optimize the ordering process, and support decisions to negotiate prices. Thus, predictive analytics in healthcare aims to improve productivity and efficiency.

Medical Imaging for Accurate Diagnosis: Predictive Analytics is already creating wonders in healthcare. Artificial Intelligence (AI) tools are being used to gather information from images, scans, X-rays, etc., to identify and highlight minor illnesses before they grow into life-threatening critical conditions. By providing treatment in advance, the illness can be controlled and monitored before it puts the health of the patient at risk. This is specially used in cancer in patients who have better chances of recovery and how they can be helped to get rid of the deadly disease. With active research going on with the use of predictive analytics in healthcare imaging, we can see more advantages of investing in this advanced technology

Monitoring for Patients at Home: Predictive analytics is not only limited to the hospital setup alone; it can be used for patients who stay at home through continuous healthcare services. Most at-risk patients stay at home for several reasons rather than in hospitals. But not many healthcare centers can provide long-term plans to assist these patients and improve their health outcomes.

Use cases of Predictive Analytics in Healthcare

FutureAnalytica is an automated machine learning platform that offers no-code AI solutions. The platform helps to determine and solve health problems by finding the best predictive models for your data. Below are some of the use cases of predictive analytics in the healthcare sector. One of the major benefits of predictive analytics is its effectiveness in data processing and analysis, which can transform the industry.

Diagnosis: Predictive analytics, is used for determining the correct condition of the patient based on the predicted progression of their state

Prognosis: is based on historical and current data, predictive analytics can help to predict how the condition will progress and the response to a specific treatment

Designing Treatment Course: Predictive analytics based on the diagnosis and the prognosis can help to determine the correct course of action, when it comes to patient treatment most effectively

Clinical Decision Support: Predictive analytics helps physicians to act at the right time and seize hold of the opportunity to help the patient with proper clinical support

Remote Monitoring: Predictive analysis can be easily conducted remotely with the right equipment

Reduce Unfavorable Events: By using predictive analytics in healthcare, you can help to detect the potential unfavourable events, like medication side effects, chronic disease exacerbation, and others, thus offering a chance to avoid them

Improve Care quality: Predictive analytics can increase the efficiency and accuracy of care provided, thus making it more efficient with good quality than the other low-based alternatives

Genetic Screening for anticipating Neonatal Pathologies: Genetics and neonatal care are the areas that are largely impacted by predictive analytics. However, the human genome is a complex system, analyzing genetic information is a complicated process. It can be used to analyze and compare the person’s genetic data with a database of possible defects and illnesses connected to them. 

How Future Analytica can help in this journey?

FutureAnalytica is the only holistic automated machine-learning, no-code AI platform providing end-to-end seamless data-science functionality with data-lake, AI app-store & world-class data-science support, thus reducing time and effort in your data-science and Artificial Intelligence journey.

FutureAnalytica enables you to use big data, machine learning tools and techniques, and predictive models to help organizations predict future results more accurately, for better decision making in real-time situations and discover future opportunities in demand faster. The different solutions we provide for analyzing the data for better prediction.

  • Forecasting and Managing admissions
  • Prediction of High-Cost Services
  • Modelling Healthcare-Associated Infections (HAIs)
  • Medication Adherence Monitoring
  • Pharmaceutical Planning
  • Claims Fraud Detection

Conclusion:

With FutureAnalytica, you can analyze patient experience with both structured and unstructured data, such as doctor observations or notes. Predictive analytics has a significant role to play in the future of healthcare. Technology is playing a vital role in the world today and, all sectors are benefitting from what it has to offer. The healthcare sector is no exception.

It can benefit significantly from predictive analytics, and this technology is a core aspect of the future of medicine and healthcare. Millions of people across the world are gaining benefits from this technology adoption, with patients able to enjoy an improved service that anticipates challenges and addresses them proactively. Diagnosis would be more accurate as well as the treatment that follows.

We hope this article was insightful and helped you understand how healthcare data can analyze patients' paths with a graphical interface, which is often needed. Also, to highlight trends and threats in patterns creating predictive models and their importance. Thank you for showing interest in our blog and if you have any questions related to Predictive Analytics, Automated Machine Learning, or AI-based platforms, please send us an email at info@futureanalytica.com. 

 

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