Open Source Business Intelligence Tools For Health Insurance Innovations

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Open Source Business Intelligence Tools For Health Insurance Innovations – Big data has changed the way we manage, analyze and use industry data. One of the most notable areas where data analytics is making big changes is healthcare.

In fact, healthcare analytics has the potential to reduce treatment costs, predict epidemic outbreaks, prevent preventable diseases and improve the overall quality of life. Average human life expectancy is increasing in the world population, posing new challenges to current treatment methods. Healthcare professionals, just like business people, can collect large amounts of data and find the best strategies to use these numbers.

Open Source Business Intelligence Tools For Health Insurance Innovations

Open Source Business Intelligence Tools For Health Insurance Innovations

In this article we will address the need for big data in healthcare and hospital big data: why and how can it help? What are the barriers to adoption? Below we look at 24 examples of big data in healthcare that already exist and that medical institutions can benefit from.

Single Source Of Truth: What Is It And Why Is It Essential?

What is Big Data in Healthcare? Big data in healthcare is a term used to describe massive volumes of information created through the adoption of digital technologies that collect patient records and help manage hospital performance; otherwise too large and complex for traditional technologies. The application of big data analytics in healthcare has many positive results and also saves lives. In essence, big-style data refers to the large amounts of information created by the digitization of everything that is consolidated and analyzed using specific technologies. Applied to health care, it will use specific health data of a population (or a certain individual) and potentially help prevent epidemics, cure diseases, reduce costs, etc. Now that we are living longer, treatment models have changed, and many of these changes are driven by data. Doctors want to understand everything they can about a person and detect the warning signs of serious illness as early as possible in their life when they arise: treating any illness at an early stage is much easier and less expensive. By using KPIs in healthcare and healthcare data analysis, prevention is better than cure, and getting a complete picture of one will enable insurance to provide a customized package. This is the industry’s attempt to address the silo problems that a patient’s data has: everywhere pieces of it are collected and stored in hospitals, clinics, surgeries, etc., with the inability to communicate properly. That said, the number of sources from which healthcare professionals can obtain information about their patients continues to grow. This data usually comes in different formats and sizes, which poses a challenge to the user. However, today’s focus is no longer on the “size” of data, but on its intelligence. With the help of the right technology, data can be intelligently and quickly extracted from the following sources in the healthcare sector: Patient portals Research studies EHR Wearable devices Search engines Generic databases Government agencies Payer registries Staff schedules Patient Waiting room In fact, for years collect huge amounts of data for medical use was expensive and time consuming. With today’s ever-improving technologies, it is becoming easier to not only collect this data, but also to create comprehensive health care reports and turn them into relevant critical insights that can be used to deliver better care. This is the goal of data analytics for healthcare: to use data-driven results to predict and solve a problem before it’s too late, but also to evaluate methods and treatments faster, track inventory better, help more patients in their own health to involve and empower. the tools to do so 24 applications of big data in healthcare Now that you understand the importance of big data in the healthcare industry, let’s explore 24 real applications that demonstrate how an analytical approach can improve processes, improve patient care and, ultimately, save lives.1) Patient Predictions to improve staffing For our first example of big data in healthcare, we will look at a classic problem that every shift manager faces: how many people do I staff in a given period? If you hire too many workers, you run the risk of adding unnecessary labor costs. With too few workers, you can have poor customer service results, which can be fatal for patients in that industry. Big data is helping to solve this problem, at least in some hospitals in Paris. A white paper from Intel describes how four hospitals that are part of the Assistance Publique-Hôpitaux de Paris have used data from various sources to produce daily and hourly forecasts of how many patients are expected to be at each facility. One of the key datasets is 10-year records of hospital admissions, which data scientists analyzed using “time series analysis” techniques. These analyzes showed researchers relevant patterns in admission rates. Then they could use machine learning to find the most accurate algorithms that predict future admissions trends. Summarizing the product of all this work, the data science team developed a web-based user interface that predicts patient load and helps with resource allocation planning with the help of online data visualization that achieves the goal of improving the general care of patients.2) Electronic Health Records (EHRs) It is the most widespread application of big data in medicine. Each person has their own digital record, including demographics, medical history, allergies, lab test results, and more. Records are shared with secure information systems and are available to public and private sector providers. Each record is composed of an editable file, which means that doctors can implement changes over time without paperwork and without the danger of data replication. EHRs can also trigger alerts and reminders when a patient is due for a new lab test or keep track of prescriptions to see if they’ve followed doctors’ orders. While EHRs are a great idea, many countries are still struggling to fully implement them. The US has made a big leap, with 94% of hospitals adopting EHRs according to this HITECH survey, but the EU still lags behind. However, an ambitious directive from the European Commission should change that. Kaiser Permanente leads the way in the US and can provide a model for the EU. They have fully implemented a system called HealthConnect that shares data across all their facilities and makes EHRs easier to use. A McKinsey report on big data healthcare analytics states that “The integrated system improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and laboratory tests.” 3) Real-time warning Other examples of data analysis in health. share a crucial functionality: real-time alerts. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing advice to healthcare professionals as they make prescriptive decisions. However, doctors want patients to stay away from hospitals to avoid costly internal treatments. This is already trending as one of the buzzwords in business intelligence in 2021 and has the potential to be part of a new strategy. Wearables will continuously collect patient health data and send this data to the cloud. In addition, this information will be accessible in the database on the health status of the general public, allowing doctors to compare this data in a socio-economic context and adjust delivery strategies accordingly. Institutions and healthcare managers will use sophisticated tools to monitor this massive flow of data and react when the results are disturbing. For example, if a patient’s blood pressure rises alarmingly, the system will send a live alert to the doctor, who will then take steps to reach the patient and take measures to lower the pressure. Another example is Asthmapolis, which started using inhalers with GPS trackers to identify asthma trends both at the individual level and in larger populations. This data is used in conjunction with CDC data to develop better treatment plans for people with asthma 4) Improving patients Many consumers – and therefore potential patients – are already interested in smart devices that record every step they take . your heart rate, sleeping habits, etc. All this vital information can be combined with other traceable data to identify potential health risks. Chronic insomnia and an increased heart rate, for example, can indicate a risk of future heart disease. Patients are directly involved in monitoring their own health and health insurance incentives can push them to lead a healthy lifestyle (eg giving money back to people who wear smart watches). Another way to do this is with new wearables in development, tracking specific health trends and transmitting them to the cloud where doctors can monitor them. Patients who suffer from asthma or high blood pressure can benefit from it, become a little more independent and reduce unnecessary doctor visits 5) Prevent opioid abuse in the US. Here is one

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