Big Data Analytics by Venkat Ankam Stay ahead with the world's most comprehensive technology and business learning platform. With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more. Real-Time Analytics with Spark Streaming and Structured Streaming Spark Streaming supports real-time processing of fast moving, streaming data to gain insights for business and make business decisions in real-time or near real-time.
Clinical decision support Here, providers have two options, which can be used separately or together — evidence-based medicine and diagnosis support. Evidence-based medicine is driven by insights extracted from health data mostly diagnosis, procedure and treatment combined with a knowledge base with similar cases, and used to find the most fitting treatment for each patient as well as to predict and avoid possible exacerbation, complication and readmission risks.
Diagnosis support, in turn, processes symptoms, lab results and patient history details to suggest possible conditions and procedures to confirm the disease, which assists in achieving timely treatment, balanced length of stay and positive health outcomes.
Safeguarding clinical trials Patient health data can be used to analyze existing clinical trials to improve trial design and eligible patient finding. Providers can match prospective treatment with fitting patients better, reducing trial failures and negative health outcomes.
Workflow improvement Quality insurance teams harness health data analytics to evaluate performance, understand clinical processes better and identify bottlenecks in care quality. They use information about procedures, primary and secondary diagnoses as well as lab tests to initiate process improvement activities, then monitor ongoing initiatives and their efficiency to ensure sustainable changes.
For example, an increased number of C-sections can be rooted in simple coincidence and completely justified or unnecessary. Inpatient alerting Alerting caregivers about changes in patient health status is critical for inpatient setting and care areas we defined for inpatient monitoring section.
The systems acquiring vitals continuously analyze inbound data and warn health specialists about negative and positive trends, critical declines and peaks, so that surgery, post-surgery recovery or any other rehabilitation process would pass on smoothly.
Fraud prevention Healthcare organizations can reduce improper billing and avoid erroneous or fraudulent claims on a pre-adjudication basis, not risking reputation and financials.
To achieve that, the transaction data with claims and billing records is analyzed to find patterns indicating fraudulent activity or other irregularities, resulting in waste and abuse. According to Mike Cottle et al. Population health management Data analytics can be used in multiple ways to benefit population health, but researchers concentrate on two dimensions — disease surveillance and chronic disease management.
Under disease surveillance, providers are analyzing diagnoses in the course of time to determine disease outbreaks and ensure speed response to them.
Chronic disease management is one of the most important goals in population health, especially in terms of reducing hospital readmissions. Patient profiling Researchers in McKinsey note that health data analytics is helpful in patient outreach.
Health data analysis methods: Time-proven and prospective Before we talk about emerging health data analytics methods, there are four baseline methods that allow caregivers to analyze clinical performance and outcomes through the prism of patient health information: Descriptive analytics Descriptive analytics allows providers to focus on current clinical issues and look into the reasons of improved or decreased outcomes.
For example, caregivers may analyze how many patients need a pneumococcal vaccine or the number of diabetes patients with blood glucose under control. Predictive analytics The most frequent issue healthcare organizations are to solve within value-based care approach, is readmissions. Therefore, providers want to make sure that the percent of patients returning to the hospital will be as low as possible and use predictive analytics to draw a possible percentage.
This data can help foreseeing the emergency room utilization. Prescriptive analytics Prescriptive analytics implies helping caregivers measuring and managing patient population health, like focusing patients with obesity and diabetes and assess their LDL levels or other measurements.
WHO has multiple tools for prescriptive analytics and population health monitoring, e. Comparative analytics Comparative analysis allows caregivers to evaluate health outcomes of individual patients with similar diagnoses but different LOS, treatment, procedures and other health data. Time-proven analysis techniques We can also define a number of effective techniques within the four-piece group of general health data analysis methods above: It helps to discover patterns and trends in patient health data allowing to define underlying processes leading to diseases, such as recurrent episodes of skin rash, stomachache, hypertension, etc.
This technique allows finding patterns and trends indirectly, by extracting quantitative parameters from unstructured text data — such as EHR entries in free text. Online analytical processing OLAP. OLAP is a set of tools allowing providers to analyze data in multiple dimensions simultaneously because it deals with preaggregated datasets.
Online analytical processing uses three types of operations: Slice-and-dice to extract a data subset and view it under different angles looking at patient population in month, location or facility dimension is slicing, where choosing a few dimensions together is dicing Drill-down to focus on additional details e.
Roll-up, the opposite of drill-down, used to consolidate information.Big information Analytics past Hadoop is an imperative source for everybody who desires to succeed in the innovative of huge facts analytics, and remain there: practitioners, architects, programmers, facts scientists, researchers, startup marketers, and complicated scholars.
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How does in-memory processing of data for real-time streaming analytics, versus first writing to storage, change the way we need to think about applications?
Steve Wilkes Steve Wilkes: It turns out we are producing way too much data to store and analyze afterwards. Choose Send data. Without leaving the KDG page, navigate to the Kinesis Analytics console to view the status of the application processing the data in real time.
When you are happy with the amount of data sent, choose Stop Sending Data to Kinesis.I recommend waiting until at least 20, records are sent.