News Article | May 11, 2017
Discovery Health Partners, a provider of payment and revenue integrity solutions for healthcare payers, will host a webinar May 18, 2017, at 12:30 p.m. CST to examine top trends in healthcare payment integrity and how they are impacting the way health plans manage the function. Titled, “Top 8 trends in payment integrity—2017,” the webinar highlights observations of the last year gleaned through hundreds of encounters with health plans by Discovery President Paul Vosters, and David Grice, VP of Strategic Account Development, and others within the Discovery team. “Increased interest in protecting the bottom line is clearly driving increased interest in payment integrity,” notes Vosters. “We see payers exploring prepayment review as a cost avoidance strategy, questioning the performance of their payment integrity function, and rethinking their insourcing/outsourcing strategies.” Vosters and Grice both report that payment integrity has captured the attention of the C-suite based on its ability to impact plan performance. These and other trends will be explored in the webinar. Payment integrity is defined as the accuracy of the transaction that occurs between payer and provider. Payment integrity ensures that claims are paid correctly—by the responsible party, for eligible members, according to contractual terms, not in error, and free of wasteful or abusive practices. In our complex and dynamic healthcare environment, realizing good payment integrity is a challenge for both payers and providers. Seats remain available for this event, hosted by Healthcare Education Associates and open to all members of RISE. Nonmembers may participate for a fee. To register, visit Webinar Registration here. About Discovery Health Partners Discovery Health Partners, a division of LaunchPoint, offers payment and revenue integrity solutions that help health payers improve revenue, avoid costs, and enhance the member experience. We offer a unique combination of deep healthcare expertise and analytics-powered technology solutions to help our clients improve operational efficiency, achieve financial integrity, and generate measurable results. More information about our solutions, including Coordination of Benefits, Eligibility, Medicare Secondary Payer Validation and Subrogation is available at http://www.discoveryhealthpartners.com.
News Article | May 16, 2017
Silicon Valley analytics software company FICO (NYSE: FICO) today announced that Discovery Health in South Africa is using the FICO® Decision Management Suite to process as many as 100,000 transactions a day. As South Africa's largest administrator of medical schemes with more than 3.2 million beneficiaries, Discovery Health uses FICO® Blaze Advisor® decision rules management system to assist in various processes, from insurance applications to claims processing, in order to accelerate decisions, save costs and improve the customer experience. Using FICO Blaze Advisor, part of the FICO® Decision Management Suite, Discovery Health can instantly and automatically determine the eligibility of an insurance applicant and their dependents, set terms such as the waiting periods to be imposed and determine offers and requirements for acceptance (e.g., medical evidence, referrals to specialists). Underwriters and other staff can update decision strategies quickly, without the need for IT assistance. Discovery Health has been using the FICO system since 2012, when it selected FICO Blaze Advisor to replace its previous rules management system and consolidate underwriting decision-making in a single system. Discovery Health wanted to make decisions in real-time across platforms and channels. The manual processing of up to 22,000 paper applications per month across 19 medical schemes required 180 staff. "We needed a best-in-class decision engine to improve our performance," said Kris Tokarzewski, chief information officer at Discovery Health. "FICO provided the most powerful decision engine, and it has helped us streamline our business and provide better service to customers." Using FICO Blaze Advisor to automate processing for 30 medical conditions, Discovery Health has automated around 40 percent of the previously manual underwriting effort for paper applications, each of which took two to four days to process. Online channels now have the ability to issue near real-time underwriting decision. In 2013, only 11 percent of health applications were automatically underwritten; today, 100% of online and paper applications are adjudicated by FICO Blaze Advisor, with a 78 percent auto-adjudication rate. "The healthcare and medical aid industries form a complicated ecosystem," said Derick Cluley, who oversees FICO's operations in Africa. "Running multiple schemes in multiple languages and multiple countries demands the kind of sophisticated decision management that FICO offers. FICO is proud of the part our technology is playing in driving down the cost of healthcare by driving up efficiencies and scale at South Africa's largest administrator of medical schemes, medical aid scheme just over 3.2 million beneficiaries." Discovery Health is part of Discovery Limited, a South African-founded financial services organization that offers a range of products including medical aid administration, life insurance, credit cards and investments, underpinned with Vitality rewards. Vitality is the world's largest scientific-based wellness solution for individuals and corporates. Vitality members get added rewards when they integrate Vitality with other Discovery products. Founded in 1992, Discovery is a shared-value insurance company whose purpose and ambition are achieved through a pioneering business model that incentivises people to be healthier, and enhances and protects their lives. The company currently covers over 5.1 million clients across South Africa, the United Kingdom, the United States, China, Singapore and Australia FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 170 US and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 2.6 billion payment cards from fraud, to helping people get credit, to ensuring that millions of airplanes and rental cars are in the right place at the right time. FICO and Blaze Advisor are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/south-africas-discovery-health-streamlines-business-using-fico-decision-management-suite-300457417.html
Health Discovery | Date: 2011-08-29
Biomarkers are identified by analyzing gene expression data using support vector machines (SVM), recursive feature elimination (RFE) and/or linear ridge regression classifiers to rank genes according to their ability to separate prostate cancer from normal tissue. Proteins expressed by identified genes are detected in patient samples to screen, predict and monitor prostate cancer.
Health Discovery | Date: 2011-03-16
A system and method for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Pre-processing data may involve transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the data that may be derived. The learning machine is therefore trained with the pre-processed training data and is tested with test data that is pre-processed in the same manner. The test output from the learning machine is post-processed in order to determine if the knowledge discovered from the test data is desirable. Post-processing involves interpreting the test output into a format that may be compared with the test data. Live data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine may then be post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.
Health Discovery | Date: 2013-07-02
A method is provided for unsupervised clustering of data to identify pattern similarities. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on similarity, with the greatest similarity giving the highest correlation score. A distribution of the scores is plotted for each k. The highest value of k that has a distribution that remains concentrated near the highest correlation score corresponds to the number of classes having pattern similarities.
Health Discovery | Date: 2015-06-29
Biomarkers are identified by analyzing gene expression data using support vector machines (SVM) to rank genes according to their ability to separate prostate cancer from normal tissue. Expression products of identified genes are detected in patient samples, including prostate tissue, serum, semen and urine, to screen, predict and monitor prostate cancer.
Health Discovery | Date: 2013-06-19
A system and method for computer-assisted karyotyping includes a processor which receives a digitized image of metaphase chromosomes for processing in an image processing module and a classifier module. The image processing module may include a segmenting function for extracting individual chromosome images, a bend correcting function for straightening images of chromosomes that are bent or curved and a feature selection function for distinguishing between chromosome bands. The classifier module, which may be one or more trained kernel-based learning machines, receives the processed image and generates a classification of the image as normal or abnormal.
Health Discovery | Date: 2012-03-12
Gene expression data are analyzed using learning machines such as support vector machines (SVM) and ridge regression classifiers to rank genes according to their ability to distinguish between BPH (benign prostatic hyperplasia) and all other conditions. Results are provided showing the correlation of results obtained using data from two independent studies that took place at different times using different microarrays. Genes are ranked according to area-under-the-curve, false discovery rate and fold change.
Health Discovery | Date: 2013-06-10
Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.
Health Discovery | Date: 2011-04-04
A method for enhancing knowledge discovery from a dataset uses visualization of a subset features within a dataset that provide the best separation of the dataset into classes. One or more classifiers are trained using each subset of features and the success rate of the classifiers in accurately classifying the dataset is calculated. The success rate is converted into a ranking that is represented as a visually distinguishable characteristic. One or more tree structures may be displayed with a node representing each feature, and the visually distinguishable characteristic is used to indicate the scores for each feature subset. Connectors between the nodes may be used to indicate unconstrained and constrained feature sets. Nodes within a constrained path may be substituted for a feature within the preferred, unconstrained path if that feature is impractical to measure.