claims data

Chilmark Research, a global research and advisory firm, recently released a report rating vendors and solutions in the healthcare analytics industry. IBM Watson Health, a leader in healthcare analytics, has put together this infographic comparing how its solutions stack up against some of the closest competitors in the industry in areas such as population discovery and definition, predictive analytics, cost and utilization, and claims data contribution.

The largest national multiline insurance had built a repository of Insurance policies (P&C and Life Insurance) on Microfilm and Microfiche in early 90’s, as a preservation strategy. They were grappling with issues as this technology became outdated over time: • Risk of losing their only source of data for Insurance policies and corresponding communication, need to improve data availability and speed of claims evaluation • Compliance issues, need of a WORM (write once read many) storage compliant with FINRA regulations, where data should be encrypted when at rest • Total cost for digitization compared to 10-12 years of support left to maintain insurance policies was not very encouraging • Required a low cost, cloud-based, FINRA-compliant document management solution which could provide quick access to stored data Download complete case study to know how LTI’s e-Office sDownload full case study to know how LTI’s e-Office solution enabled 50% TCO for Largest national Multiline Insurance.

Join RelayHealth for a recorded Healthcare Finance News webinar, Accelerating Service-to-Payment Velocity. With all of the changes happening in healthcare today, some things do remain the same. Your two primary sources of cash are still patients and third-party payers. While patient financial responsibility is rapidly increasing, a large percentage of revenue still flows in via governmental payers and commercial health plans.

When the IT team determined the need for a new tool, they downloaded Splunk and saw immediate value. Read more about HealthTrans' hard and soft ROIs by integrating Splunk into their IT infrastructure.

Learn how Life Sciences organizations can accelerate Real World Evidence by achieving faster time to insight with a metadata-driven, semantically enriched operational platform. Real World Evidence (RWE) is today’s big data challenge in Life Sciences. Medical records, registries, consultation reports, insurance claims, pharmacy data, social media, and patient surveys all contain valuable insights that Life Sciences organizations need to ascertain and prove the safety, efficacy, and value of their drugs and medical devices. Learn how Life Sciences organizations can accelerate RWE with a metadata-driven, semantically enriched operational platform that enables them to: • Unify, harmonize and ensure governance of information from diverse data sources • Transform information into evidence that proves product efficacy and safety • Identify data patterns, connections, and relationships for faster time to insight

A Study of Actual Payouts for Covered Data Breaches.

Lots of marketing claims are being made by Cloud Data Protection Gateway providers. It’s time for critical analysis. This Blue Coat paper reviews the key questions Security, Governance & Risk and IT should be asking vendors.

The insurance industry stands on the precipice of change, with waves of innovation and disruption driving new possibilities across all departments, including pricing, underwriting, claims, and fraud. This webinar recording of a live panel debate is ideal for insurance professionals wanting to understand how best to unlock the possibilities created by advanced analytical techniques such as Artificial Intelligence (AI), Machine Learning (ML), and others. This TIBCO and Marketforce webinar on “The Fourth Industrial Revolution in Insurance” includes speakers Ian Thompson, chief claims officer at Zurich; David Williams, chief underwriting officer at AXA; and Clare Lunn, GI fraud director at LV=. The panel discusses: Moving towards the algorithmic insurer: the opportunities created by AI and ML How insurers can become more agile in the face of new innovations and disruptive technologies How the industry can turn structured and unstructured data into insights

With the combination of electronic health records, rich repositories of claims data, medical device outputs, laboratory and prescription systems, real-world data and the data mined from other information technology systems, the health and life sciences ecosystem can now gain new perspective. Download this complimentary paper to learn more about how health care data has the power to transform the sector, helping to address the industry’s biggest challenges surrounding costs and quality of patient care. By adopting solutions that allow them to both produce and consume data analytics insights in a way that better guides clinical and business strategies, innovative health care organizations can learn not only to survive but also thrive in the decades to come.

With the combination of electronic health records, rich repositories of claims data, medical device outputs, laboratory and prescription systems, real-world data and the data mined from other information technology systems, the health and life sciences ecosystem can now gain new perspective. Download this complimentary paper to learn more about how health care data has the power to transform the sector, helping to address the industry’s biggest challenges surrounding costs and quality of patient care. By adopting solutions that allow them to both produce and consume data analytics insights in a way that better guides clinical and business strategies, innovative health care organizations can learn not only to survive but also thrive in the decades to come.

Insurers lose millions each year through fraudulent claims. Learn how leading insurance companies are using data mining techniques to target claims with the greatest likelihood of adjustment, improving audit accuracy and saving time and resources. Read this paper to learn how to combine powerful analytical techniques with your existing fraud detection and prevention efforts; build models based on previously audited claims and use them to identify potentially fraudulent future claims; ensure adjusters focus on claims most likely to be fraudulent; and deploy results to the people who can use the information to eradicate fraud and recoup money.

In a three-year retrospective study that analyzed health care claims and wearable device data from a self-insured employer, Springbuk looked at the potential impact of Fitbit technology as part of a wellness program. The analysis shows that connected health and fitness interventions can close the gap between the everyday actions that change health and drive outcomes for employee health and employer cost. Some of the key findings include: * Employees who opted into the Fitbit program demonstrated significant cost savings when compared to the control group. * Engaged users cost less than non-engaged users. * The opportunity for cost savings is potentially the highest with less active individuals.

Fear not, it is possible to prove the return on investment (ROI) of your wellness program! But don’t take our word for it. Springbuk, a health intelligence platform, recently evaluated the health care claims and wearable device data for a self-insured employer (and Fitbit customer) over a three-year period. View this webinar, Rod Reasen, CEO at Springbuk, walks you through their study and reveals how: - Employees who opted into the company’s wearable program cost less than their counterparts - Total costs for engaged wearable users dropped by 46% vs. 14% for non-engaged individuals - Cost reductions were highest with less active members – encouraging news for all employee populations

This case study will showcase the challenges of clinical trial data management, but the highlighted techniques are applicable to any business needing smart, dynamic and adaptive Web-based user interfaces.

Trupanion, a Seattle-based medical insurance provider for cats and dogs, needed to find data insights quickly. With only 1% of pet owners insured, the process of evaluating a claim to approve or deny payment was manual and time-consuming. Building accurate predictive models for decision-making required manpower, time, and technology that the small company simply did not have. DataRobot Cloud, built on AWS, helped Trupanion create an automated method for building data models using machine learning that reduced the time required to process claims from minutes to seconds. Join our webinar to hear how Trupanion transformed itself into an AI-driven organization, with robust data analysis and data science project prototyping that empowered the company to make better decisions and optimize business processes in less time and at a reduced cost. Join our webinar to learn: Why you don’t need to be an expert in data science to create accurate predictive models. How you can build and deploy pr

“Big data”– which admittedly means many things to many people – is no longer confined to the realm of technology. Today, it is a business imperative. In addition to providing solutions to insurance companies’ long-standing business challenges, big data inspires new ways to transform processes, organizations and many aspects of the insurance industry as we know it.

Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media

Learn how to use surveys to test marketing claims by taking the guesswork out of your big decisions.

The general public is becoming increasingly cynical about the environment claims of businesses and with much bad press around data centers, information technology (IT) is in the front line. But data centers are actually the easiest bit of IT to control and consolidating infrastructure into them can help reduce the overall energy usage of IT and, if used well, IT itself can help businesses reduce their overall carbon footprint.

Case Study: Beyond "Clinically Enriched Administrative Data": Using Patient- Specific Datasets to Drive Rapid Clinical & Financial Improvements