Healthcare cost Archives - InnoHEALTH magazine

Machine Learning in Claims Processing

InnoHEALTH Magazine — Wed, 30 Oct 2019 07:57:09 +0000

Challenge

Healthcare costs across the world have soared over the past decades at a rate at which, where United States spends more on healthcare than the national budget of half of the countries in the world. A big chunk of those expenses relate to pharmaceutical products.

AI and IoT in Healthcare: Need of Future
Opportunity

Here is the opportunity for Machine Learning Solutions – in US, by and large, most pharmaceutical transactions are captured electronically as claims. These claims, and the way they are processed, cover a myriad scope of information including patient demographics, disease states, drug utilization review, formularies, coverage and utilization review, contra-indications, etc. This information can be used for a number of reasons – better pricingof drugs based on their utilization and volume, better prediction of diseases and therapeutic journeys where we can guess over time which drugs a patient will require, and a more interactive engagement of the patient using virtual “friends” to guide them through their therapy and ensure compliance, adherence and better outcomes.

IoT can truly Transform Rural Healthcare in India

Over the past several years, solutions have been operationalized working with payors, PBMs on cost and efficacy predictions for new therapies for HIV (PrEP treatment) and Hemophilia (Gene Therapy). Another application is successfully helping Pharmacy Benefit Managers predict their most efficient drug pricing for patients who are not covered with insurance, cost efficiencies for seasonal and style drugs to name a few. GxWave™ leverages LSTM algorithm (Long Short-Term Memory) in predicting price efficiencies, claim volumes, call center call volumes, and average margins.

Approach

Solutions such as GalaxE’s GxWave™ platform with solutions such as Claims Neurology described below, utilizes their proprietary technology to extract business rules from adjudication systems and then use prior claims data to predict various “edits” or applicable rules such as prior authorization (where the use of a specific drug requires express approval from the physician) or adjustments and accumulators so that across their therapy, the price they pay is properly adjusted for the full range of medicines and medical services the patient consumes. These pathways for the adjudication of a claim are then trained on neural nets that learn the time based, formulary based, disease and therapy-based trends.

AI and Cybersecurity in Digital Healthcare

The trained nets are then used to predict and project the therapeutic journey of a patient, or the volume and timeframe for the consumption of specific therapy in a given market.

With all the consolidations of drug & device industries in play, their efficiency has to be at the highest point in order to drive patient costs lower. GxWave™ is helping these entities with improving efficiency. With a combined data set comprising elements of documentation across the development lifecycle of an application with governing procedures and its intended use, natural language processing techniques can derive information from previously unexplored data sets that can be analyzed to ensure compliance to regulations, adherence to organization policies and procedures and alignment with the documented intended use of the system. Healthcare IT consists of diverse applications with multiple critical integrations at various levels where regulatory impact can be ambiguous and could be left unassessed. A change in the landscape has to be simultaneously assessed for regulatory and risk impacts (includes business, security and privacy risks) without delays ensuring all impacts are being planned for before the change is implemented.

Healthcare IT market builds the foundation of Artificial Intelligence
Advantages
A number of advantages have surfaced with these solutions:
1. Immediate re-categorization or inspection of current non-regulated applications that could potentially be regulated due to change in feature/functionality.
2. Expose deficiencies within the system that could lead to a potential replacement for not being able to satisfy customer and regulatory requirements.
3. Allow for continual monitoring instead of the traditional approach of conducting periodic reviews.
4. Improve software documentation quality across the application lifecycle.

This is just the beginning. We expect that solutions like GxWave™ will utilize data from genomics, patient profiles, therapy histories and help generate the most medically and economically rational and effective therapies for patients in the very near future!

About the authors

Sandipan Gangopadhyay is the President and COO of GalaxE and plays a key role in GalaxE’s continued worldwide expansion and operational success Prior to this, Mr. Gangopadhyay spent over a decade in high profile roles in both Pharmaceutical and Information Technology companies around the globe and instrumental in setting up one of India’s first private Software Technology Parks. He has a Bachelor’s degree in Computer Engineering from Bombay University, is a member of the Indian Institute of Chemical Engineers, and is certified in the Governance of Enterprise IT.

Dheeraj Misra is the Chief Technical Officer and Senior Executive Vice President of GalaxE and has over 15 years of experience in the design, development, testing, porting and maintenance of application and system software for the healthcare industry. Prior to this, he spent a number of years in high profile roles at HCL Technologies, Context Integration, Eforce Global, and as a Research Specialist in Parallel Processing. He has a B.E. in Computer Engineering from REC, Allahabad.

Vijayaraj Chakravarthy, Senior Vice President of Delivery and Head of Strategic Business Unit, is a member of the GalaxE’s executive leadership team, responsible for organization growth and innovation through predictive analytics platform aiming to discover new cost reduction opportunities in the Healthcare ecosystem of the United States. He serves as a subject matter expert on various AI/ML-based platforms and frameworks. He is passionate about solving industry problems with automation methods and agile execution. His leadership style focuses on developing a positive environment, teamwork, passionate culture, and an entrepreneurial mindset. He has B.E in Electrical and Electronics from PSG College of Technology, India. He enjoys spending time with his family. He has a green thumb to nurture organic gardens. He likes to do tech-inspired research projects for Kids.

The post Machine Learning in Claims Processing appeared first on InnoHEALTH magazine.

Stressful events can increase women's obesity

InnoHEALTH Magazine — Tue, 03 Apr 2018 08:33:59 +0000

Women who experienced one or more traumatic lifetime events or several negative events in recent years had higher odds of being obese than women who didn’t report such stress, according to preliminary research presented at the American Heart Association’s Scientific Sessions 2017, a premier global exchange of the latest advances in cardiovascular science for researchers and clinicians.

“Little is known about how negative and traumatic life events affect obesity in women. We know that stress affects behaviour, including whether people under- or overeat, as well as neuro-hormonal activity by in part increasing cortisol production, which is related to weight gain,” said study senior author Michelle A. Albert, M.D., M.P.H., professor of medicine, cardiology, and founding director of the Center for the Study of Adversity and Cardiovascular Disease, at University of California, San Francisco.

Obesity, a preventable risk factor for cardiovascular and other diseases, impacts more than one-third of U.S. adults. According to the American Heart Association, nearly 70 percent of American adults are either overweight or obese. Women tend to live longer than men, putting especially obese, aging women at greater risk for disease, said study author Eva M. Durazo, Ph.D., a post-doctoral scholar at the NURTURE Center, Division of Cardiology, and UCSF said.

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The researchers studied the relationship between major life events and obesity in a group of 21,904 middle-aged and older women, focusing on women with the highest obesity prevalence. They defined obesity as having a body mass index (BMI) of 30 kg/m2 or higher. And, they measured the impacts of two types of stress: traumatic events, which could occur anytime in a woman’s life and includes events as death of a child or being a victim of a serious physical attack, as well as negative life events that had occurred in the previous five years of a woman’s life. Negative events included wanting employment but being unemployed for longer than three months or being burglarized.

Sleep deprivation may increase risk of cardiovascular disease in older women Older women who don’t get enough sleep were more likely to have poor cardiovascular health, according to preliminary research presented at the American Heart Association’s Scientific Sessions 2017. In the new study researchers considered sleeping at least two hours more during the weekend than on the weekday as a sign of being in state debt. Among the roughly 21,500 female health professionals between ages of 60 and 84 the research team followed, women who were in sleep debt were more likely to be obese and have hypertension. When taking into account socioeconomic status and sources of stress, such as negative life events and work-related stress that could also influence cardiovascular health, quality of sleep was still an important factor for good overall cardiovascular health. The results suggest that not getting enough sleep during the week might throw the body off and may increase risk of cardiovascular disease in older women.

NEARLY A QUARTER (23 PERCENT) OF THE WOMEN STUDIED WERE OBESE

Women who reported greater than one traumatic life event versus no traumatic life events had 11 percent increased odds of obesity;

The higher the number of negative life events reported by women in the last five years, the higher the tendency for increased odds of obesity. Specifically, women who reported four or more negative life events had a 36 percent higher risk of obesity, compared to women who reported no such events;

Among women who had higher levels of physical activity, there was a stronger association between increasing cumulative/chronic stress and obesity, though the reason for this finding remains uncertain.

“Our findings suggest that psychological stress in the form of negative and traumatic life events might represent an important risk factor for weight changes and, therefore, we should consider including assessment and treatment of psychosocial stress in approaches to weight management,” Albert said.

Because the study looks at the association between stressful events and obesity in a snapshot of time, future studies should look at the relationship longitudinally, following people for weight gain over time after life events have occurred, according to Albert.

“This is important work because women are living longer and are more at risk for chronic illnesses, such as cardiovascular disease. The potential public health impact is large, as obesity is related to increased risks of heart attack, stroke, diabetes and cancer, and contributes to spiraling healthcare costs,” Albert said.

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