5 Best Examples Of Big Data In Healthcare

5 Best Examples Of Big Data In Healthcare

We will then look at the 5 best examples of big data in healthcare that already exist and that medical-based institutions can benefit from. 5 Best Examples Of Big Data In Healthcare Across sectors, big data has transformed the way we handle, analyze, and exploit data. Healthcare is one of the most noteworthy domains where data analytics is having a significant impact. Indeed, healthcare analytics has the ability to lower treatment costs, forecast epidemic outbreaks, prevent preventable diseases, and improve the overall quality of life. The average human lifetime is rising over the world, posing significant obstacles to current treatment delivery techniques. Health care practitioners, like business owners, are capable of collecting large volumes of data and determining the best ways to use it.

What Is Big Data In Healthcare?

Big data in healthcare refers to vast amounts of data generated by the adoption of digital technology that collects patient records and aid in the management of hospital performance, which would otherwise be too large and complex for traditional technologies. The use of big data analytics in healthcare has a number of positive and even life-saving implications. Big-style data, in essence, refers to the massive amounts of data generated by the digitization of everything that is aggregated and analyzed by specific technology. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc. Treatment models have altered as people have lived longer, and many of these changes are mostly driven by data. Doctors aim to learn as much as they can about a patient as early as possible in their lives so that they can see warning symptoms of dangerous sickness as soon as they appear. Treating a disease early is far easier and less expensive. Prevention is better than cure when it comes to healthcare data analytics and key performance indicators, and being able to construct a thorough picture of a patient will allow insurance to deliver a customized package. 5 Best Examples Of Big Data In Healthcare

5 Best Examples Of Big Data In Healthcare

1. Patients Predictions For Improved Staffing For our first big data in healthcare example, we’ll look at a classic problem that every shift manager faces: how many employees should I put on staff at any given time? You run the danger of incurring extra labor costs if you hire too many people. When there is too little staff, customer service suffers, which can be disastrous for patients in that industry. At least at a few Parisian hospitals, big data is assisting in the solution of this problem. According to an Intel white paper, four hospitals in the Assistance Publique-Hôpitaux de Paris have been collecting data from a number of sources to anticipate how many patients will be at each hospital on a daily and hourly basis. One of the main data sets is a decade’s worth of hospital admissions records, which were crunched using “time series analysis” techniques by data scientists. The researchers were able to discover important patterns in admission rates as a result of their studies. They could then utilize machine learning to determine the most accurate algorithms for predicting admissions patterns in the future. Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients’ care. 2. Electronic Health Records (EHRs) It is the most often used form of big data in medicine. Every patient has his or her own digital record, which includes information such as demographics, medical history, allergies, and laboratory test results, among other things. Records are shared through secure information systems and are accessible to both public and private sector suppliers. Every record is made up of a single editable file, which means doctors may make changes over time without having to deal with paperwork or the risk of data replication. EHRs can also send out alerts and reminders when a patient needs a fresh lab test, as well as track prescriptions to see if they’ve been followed. Despite the fact that electronic health records are a terrific idea, many countries are still struggling to completely deploy them. According to this HITECH study, the US has made a significant jump forward, with 94 percent of hospitals using EHRs, but the EU is still lagging behind. However, it is hoped that an ambitious directive developed by the European Commission will change this. In the United States, Kaiser Permanente is leading the way, and it could serve as a model for the EU. They’ve completed the implementation of a system called HealthConnect, which shares data across all of their locations and simplifies the usage of EHRs. A McKinsey report on big data healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.” 5 Best Examples Of Big Data In Healthcare 3. Real-Time Alerting Other applications of data analytics in healthcare have one thing in common: real-time alerts. Clinical Decision Support (CDS) software analyzes medical data in real-time in hospitals, providing assistance to doctors while they make prescriptive decisions. Doctors, on the other hand, prefer that people avoid hospitals in order to avoid costly in-house therapies. Analytics, which is already one of the hottest business intelligence buzzwords for 2019, has the potential to become a new strategy. Wearables will continuously collect patient health data and send it to the cloud. Furthermore, this information will be linked to a database on the general public’s health, allowing clinicians to examine the data in a socio-economic context and adjust delivery techniques accordingly. Institutions and care managers will utilize sophisticated tools to monitor this large data stream and respond immediately if the results are alarming. 4. Enhancing Patient Engagement Many customers – and thus potential patients – are already interested in smart devices that track their every step, heart rate, sleeping habits, and other data on a continuous basis. All of this essential data can be combined with other trackable data to uncover hidden health hazards. For example, chronic sleeplessness and an increased heart rate can indicate a future risk of heart disease. Patients are directly involved in their own health monitoring, and health insurance incentives can encourage them to live a healthy lifestyle (e.g.: giving money back to people using smartwatches). New wearables under development are another way to do so, tracking specific health trends and reporting them to the cloud, where clinicians can monitor them. Patients with asthma or high blood pressure could profit from it, becoming more self-reliant and reducing unnecessary doctor visits. 5. Using Health Data For Informed Strategic Planning Because of deeper insights into people’s motives, the use of big data in healthcare enables strategic planning. Care managers can look at the results of check-ups among persons from various demographic categories to see what factors deter people from seeking treatment. The University of Florida created heat maps for a variety of topics, including population growth and chronic diseases, using Google Maps and open public health data. Academics then linked this information to the availability of medical care in the hottest places. They were able to reassess their delivery plan and add more care units to the most problematic locations as a result of the information they gained. 5 Best Examples Of Big Data In Healthcare 6. Improved Supply Chain Management Everything else, from patient care and treatment to long-term finances and beyond, is likely to suffer if a medical institution’s supply chain is weakened or fractured. As a result, the following example in our series of big data in healthcare case studies focuses on the importance of analytics in keeping the supply chain fluid and efficient from beginning to end. Hospitals can save up to $10 million per year by using analytics tools to manage supply chain performance metrics and make precise, data-driven choices about operations and spending.

Why We Need Big Data Analytics In Healthcare

There’s a huge need for big data in healthcare as well, due to rising costs in nations like the United States. As a McKinsey report states: “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP — nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.” To put it another way, costs are substantially higher than they should be and have been for the past 20 years. Clearly, we need some data-driven, smart thinking in this area. Current incentives are also shifting: many insurance companies are moving away from fee-for-service plans (which incentivize the use of expensive and sometimes useless therapies as well as serving large numbers of patients fast) and toward programs that prioritize patient outcomes. Previously, healthcare providers had no direct incentive to share patient data with one another, making it more difficult to harness the potential of analytics. Now that more of them are being paid based on patient outcomes, they have a financial incentive to share data that can help patients while also saving insurance companies money. Finally, medical decisions are becoming increasingly evidence-based, which means they rely on big swaths of research and clinical data rather than merely on their education and professional judgment. Data collection and administration, like many other businesses, is becoming more complex, and experts want assistance. Because of this changing mindset toward treatment, there is a higher demand for big data analytics in healthcare institutions than ever before, and the rise of SaaS BI tools is helping to meet that demand.

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