Speqto Technologies is at the top of the list of companies developing technical solutions for the healthcare industry. With our team of experts and using their expertise and experience in their respective fields, we have created unique solutions for our clients in the healthcare sector. Our solutions are easy to use, simple user interface, easy integration with the existing infrastructure in the organization and many other benefits.

Over the course of this blog, we will look at one technology in particular, i.e. predictive analytics and its applications, benefits and impact on the healthcare industry.

Predective Analysis Flow

The above diagram shows the work flow of the predictive analytics mechanism. The analytics and solution to a problem statement has to pass through 7 stages to complete the predictive analytics protocol :-

Predictive analytics is a collection of statistical analytical tools, techniques where historical and present data is analysed to make predictions about unknown factors. Some of the prominent techniques used in predictive analytics are data mining, predictive modelling and machine learning algorithms. In predictive analytics, the relationship between a specific performance of a unit and the features of the unit is analysed. The main goal of the initiative is to establish the probability of a specific event reoccurring through the study of historical and past data sets.

1.Problem Definition :-

The first stage of the predictive analytics model is understanding the problem statement, define the goals, and objectives of the process. Based on the information from this stage, actions of the other stages are defined and designed to work in a specific pattern.

2.Data Collection :-

Once the goals of the predictive analytics model is defined, the next stage in the process is the collection of the appropriate data sets i.e. real time and historical data sets. The historical data sets are collected as reference and training data set while the real time data set is what will be analysed to complete the task.

3.Data Cleaning :-

The collected data has numerous invalid and irrelevant fields. The primary step is to clean the data to get rid of all the redundancies and irrelevant data. The data which remains is the crucial set which will be used to train the system and will be used in the analysis process.

4.Data Analytics:-

The next stage in the predictive analytics process is the data analysis itself. Various data analytics methods such as kNN, Cluster analysis, neural networks, Regression analysis and statistical analytics are used to complete the analysis step of the predictive analytics process.

5.Predictive Modelling :-

The next stage is the prediction of the unknown values using the results of the analysis from the previous stage. This is the most important component of the entire predictive analytics process.

6.Deployment :-

The next step in the process is the deployment of the designed system to a real world where it can function based on the design, functions, aim and objectives of the system.

7.Monitoring :-

The final stage in the process is the monitoring and the evaluation of the system along with checking for the accuracy of operation. Additionally, methods to improve the performance of the system are investigated and suitable improvements are developed and deployed to the system.

Keys Used In Predictive Analytics

Predictive Analytics has a huge demand in the industry due to its high computational capabilities and the performance under real world circumstances. Predictive analytics is particularly popular in the healthcare industry due to this performance capabilities and flexibilities. Some of the popular applications in the healthcare industry are

Some of the key benefits of using this standard are mentioned below:-

1.RISK SCORING FOR CHRONIC DISEASES, POPULATION HEALTH

2. AVOIDING 30-DAY HOSPITAL READMISSIONS .

3. GETTING AHEAD OF PATIENT DETERIORATION .

4. FORESTALLING APPOINTMENT NO-SHOWS.

5. PREVENTING SUICIDE AND PATIENT SELF-HARM .

6. PREDICTING PATIENT UTILIZATION PATTERNS .

7. MANAGING THE SUPPLY CHAIN .

8. ENSURING STRONG DATA SECURITY .

9. DEVELOPING PRECISION MEDICINE AND NEW THERAPIES .

10. BOLSTERING PATIENT ENGAGEMENT AND SATISFACTION .

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1.Decision Making Process :-

Predictive analytics solutions from Speqto Technologies are designed to reduce the time taken to make a decision and using the solution, the decision making process is streamlined and efficient.

2.Improving Patient Outcomes :-

In the above section, we analysed some of the applications of predictive analytics in the healthcare industry. Softwares and solutions by Speqto Technologies are designed with one specific goal in mind, i.e. improve the patient outcomes.

3.Relief for Healthcare workers :-

One of the biggest complaints in the healthcare industry is the overworking and understaffing. Predictive analytics solutions by Speqto Technologies are designed to mitigate both these challenges. A majority of the manual labour can be automated using the model and the man power can be redirected to a position where resources are necessary.

4.Security :-

The healthcare industry is a place where millions of people express their personal and confidential information. Softwares from Speqto Technologies offer unparalleled levels of security and privacy without compromising the quality of services for the healthcare staff and the patients.

5.Scalability :-

Predictive analytics systems from Speqto are highly flexible in terms of the deployment. It can be easily deployed for an organization with just 10 people or for an organization with 10,000 people. The scalability of the systems are one of the biggest advantage.

6.Simple Integration with existing infrastructure :-

Systems designed by Speqto Technologies are easily compatible with the existing infrastructure in the organization. The existing infrastructure is considered from the early stages of the development and once completed, it can be easily deployed and integrated with the existing network.