CMS cutting-edge technology identifies & prevents $820 million in improper Medicare payments in first three years

The Fraud Prevention System is one part of the administration’s effort to protect the Medicare Trust Fund

After three years of operations, the Centers for Medicare & Medicaid Services (CMS) today reported that the agency’s advanced analytics system, called the Fraud Prevention System, identified or prevented $820 million in inappropriate payments in the program’s first three years. The Fraud Prevention System uses predictive analytics to identify troublesome billing patterns and outlier claims for action, similar to systems used by credit card companies. view history .  The Fraud Prevention System identified or prevented $454 million in Calendar Year 2014 alone, a 10 to 1 return on investment.

“We are proving that in a modern health care system you can both fight fraud and avoid creating hassles for the vast majority of physicians who simply want to get paid for services rendered. The key is data,” said CMS Acting Administrator Andy Slavitt. “Very few investments have a 10:1 return on taxpayer money.”

The Fraud Prevention System was created in 2010 by the Small Business Jobs Act, and CMS has extensively used its tools, along with other new authorities made possible by the Affordable Care Act, to help protect Medicare Trust Funds and prevent fraudulent payments. For instance, last month Health & Human Services (HHS) and the Department of Justice announced the largest coordinated fraud takedown in history, resulting in charges against 243 individuals, including 46 doctors, nurses, and other licensed medical professionals, for their alleged participation in Medicare fraud schemes involving approximately $712 million in false billings. Over the last five years, the administration’s efforts have resulted in more than $25 billion returned to the Medicare Trust Fund.

The Fraud Prevention System helps to identify questionable billing patterns in real time and can review past patterns that may indicate fraud.  In one case, one of the system’s predictive models identified a questionable billing pattern at a provider for podiatry services that resulted in Medicare revoking the provider’s payments and referring the findings to law enforcement. The Fraud Prevention System also identified an ambulance provider for questionable trips allegedly made to a hospital. offshore centre During the three years prior to the system alerting officials, the provider was paid more than $1.5 million for transporting more than 4,500 beneficiaries.  A review of medical records found significant instances of insufficient or lack of documentation. CMS also revoked the provider’s Medicare enrollment and referred the results to law enforcement.

“The third year results of the Fraud Prevention System demonstrate our commitment to high-yield prevention activities, and our progress in moving beyond the ‘pay and chase’ model,” said Dr. Shantanu Agrawal, CMS deputy administrator and director of the Center for Program Integrity. site rank “We have learned a lot in the three years since the Fraud Prevention System began, and as we learn, we continue to become more sophisticated in detecting aberrant billing patterns and developing leads for investigations and action.”

In future years, CMS plans to expand the Fraud Prevention System and its algorithms to identify lower levels of non-compliant health care providers who would be better served by education or data transparency interventions.

For more information, please see the Report under “Guidance and Reports” at:

Healthcare Big Data Analytics Plays Critical Role in Quality

The healthcare industry has plenty of big data on its hands, but the ability to extract meaningful, actionable insights from this wealth of raw information will be the key to improving quality and patient outcomes across the developing learning health system, says a new white paper from the National Quality Forum (NQF).

In order for a broader spectrum of providers to see benefits from healthcare big data analytics, healthcare organizations must commit to cultural and leadership changes that promote an analytical approach to the practice of medicine.

Healthcare big data analytics challenges

The recommendations are part of a collaboration between NQF, the Peterson Center on Healthcare, and the Gordon & Betty Moore Foundation that aims to educate, encourage, and learn from healthcare stakeholders as they enter the complex world of big data analytics.

The white paper, entitled “Data for Systematic Improvement,” summarizes a recent meeting of healthcare experts who discussed how to make big data analytics a sustainable reality for more organizations.

While some pioneering providers have made significant strides towards harnessing big data for quality and operational improvements, the questionable impact of health IT systems on patient safety and provider productivity, as well as sluggish progress on health information exchange and health data interoperability that limits communication and care coordination, has left the healthcare industry floundering for immediate, actionable solutions.

“Sustained improvement requires a systems approach that takes into account the fact that multiple clinicians and healthcare workers are involved in a patient’s care, the complexity of modern diagnostics and treatments, the different settings that healthcare is delivered (such as hospitals, out-patient clinics, skilled nursing facilities, home health, and other settings), and the different determinants of a person’s health,” says the report. “For systems improvement tools to achieve their potential, they require multiple types of data, which can identify opportunities, gauge progress, and help users understand what works.”

During the meeting, participants shared the results of big data projects that have led to quality improvements within their organizations, such as leveraging predictive analytics to identify high-risk patients, implementing evidence-based protocols to reduce infections and adverse events, and highlighting the importance of patient-centered care.

“One important finding was that simply providing data feedback can drive improvement as long as the data is timely and clinically relevant,” the paper adds. “The project participants and surveyed leaders recalled multiple cases where clinicians improved their care practices once presented with trusted, accurate, and meaningfully synthesized data.”

“This occurred because feedback leverages clinicians’ intrinsic motivation as professionals to deliver high quality care. Furthermore, data are required for the success of other incentives for better care, such as payment programs, as clinicians and healthcare organizations need timely data to understand where to improve and track their progress.”

But the eagerness of healthcare organizations to provide this type of data to their clinicians has been met with some massive and stubborn roadblocks.  Stakeholders once again identified the chronic lack of health data interoperability as a major challenge for healthcare big data analytics, noting the difficulties in collecting and linking disparate data sources from across the community into a workable data pool. Providers also struggled with enacting internal improvements based on their own quality and performance data, as well as providing feedback to relevant parties in a timely way.

Healthcare organizations continue to wrestle with issues of data integrity, including the trustworthiness, completeness, and accuracy of information gathered from EHRs and other data sources. In order to create a big data platform that allows for optimal decision-making, organizations must continue to chip away at data siloes that prevent them from fully leveraging all available sources, including administrative data, claims data, patient experience feedback, and community-level information.

The issues aren’t just technical, either. Effective change management is difficult and slow to enact, the participants admitted, and changing organizational culture to become more reliant on data is often an uphill battle.

“Workforce training is required in how to apply process improvement tools, understanding the potential and limitations of data, and analyzing data. But training is not enough,” the report says. Providers must make an effort to revolutionize “the organization’s culture, the business case, leadership commitment to using data for improvement, and communication channels that share what works.”

“In addition, several participants also noted that successful initiatives depend on clear priorities, and that clinicians and healthcare professionals feel pulled in too many directions to make significant improvement in any one area.”

Healthcare organizations that hope to benefit clinically and financially from big data analytics must tackle these challenges quickly if they are to take advantage of big data’s potential. Starting small, in one or two areas of operational improvement, may help to build interest and buy-in that starts to build the case for more widespread improvements. Providers should be sure to gather input and feedback from as many internal stakeholders as possible to ensure that they are moving in the right direction as big data continues to spark innovative tools that make meaningful quality improvements a reality.

The National Quality Forum seeks public comment on this report and its findings, due by June 15, 2015. A webinar will be held on June 30 to discuss the comments and refine recommendations related to leveraging healthcare big data analytics on a larger scale for improved quality and better outcomes. sites list how to find my ip address . domain address . site rank offshore centre expidoms . domain analysis

Healthcare Big Data Analytics: From Description to Prescription

In the healthcare industry, “big data analytics” is a term that can encompass nearly everything that is done to a piece of information once it begins its digital life.  From flagging drug interactions to predicting sepsis, modeling emergency department use to triggering an automated phone call for a mammogram reminder, healthcare providers are leveraging patient data from the EHR and elsewhere for an astounding array of patient care tasks.

But a worrying number of providers continue to struggle to understand just how huge their big data is, not to mention how to collect and use it most effectively.  Organizations are all over the map when it comes to their abilities to use healthcare big data analytics for actionable tasks like population health management and care coordination – many providers are still wrestling with how to get basic patient into their EHRs efficiently, let alone forestall a preventable hospital readmission three months down the line.

In healthcare, as in many other industries, an organization’s big data analytics capabilities can fall into three major categories: descriptive, predictive, and prescriptive.

What do these terms really mean for hospitals and other providers looking to benchmark their analytics progress, and how can they help to guide organizations towards their ultimate data-driven patient care goals?

Descriptive analytics: What has happened?

In its most basic form, healthcare data only tells you what has already happened.   How many patients were admitted to the hospital last July?  How many returned within 30 days?  How many acquired an infection or suffered from a patient safety mistake?

Descriptive analytics is the ability to quantify events and report on them in a human-readable way.  It’s the first step in turning big data into actionable insights, and there is a lot to be learned from this level of analytics.

Providers who engage in descriptive analytics have ability to generate reports that illuminate events that have already occurred, resources that have been consumed, or patients who have a new diagnosis on their charts.  This can help with population health management tasks such as identifying how many patients are living with diabetes, benchmark outcomes against government expectations, or identify areas for improvement on clinical quality measures or other aspects of care.

Yet this elementary level of reporting still remains out of reach for many organizations.  EHR data locked into narrative free-text must be extracted with the help of additional healthcare big data analytics infrastructure, which can be an expensive investment.  Proprietary data standards maintain unhelpful silos of information; a lack of available human expertise or proper organizational buy-in can leave data on the table, frozen in incomprehensible zeroes and ones.

Healthcare organizations who feel stuck on this first rung of the analytics ladder should take several steps to ensure they can progress in the future:

• Invest in interoperable EHR technology that balances free-text input for clinicians with standardized data elements for analytics and health information exchange

• Choose whether or not to develop additional analytics infrastructure in-house, turn to the cloud, or trust a third-party analytics service provider to improve reporting capabilities

• Develop a robust data governance program to ensure that data is created in a meaningful and usable way as the volume of big data continues to grow exponentially

Predictive analytics: What’s probably going to happen?

Organizations that feel they have a sufficiently complete and accurate descriptive analytics program can join the very few healthcare providers who have moved on to predictive analytics: the ability to use descriptive data to forecast what might happen in the future.

Predictive analytics is one of the hottest topics in healthcare at the moment as providers seek evidence-based ways to reduce unnecessary costs, take advantage of value-based reimbursements that no longer reward voluminous care, and avoid penalties for failing to control chronic diseases or avoid adverse events that are within their power to prevent.

Predictive analytics is so elusive for healthcare organizations because it is so much more than just reading the tea leaves of historical events.  It requires access to real-time data that allows nimble decision-making, clinically and financially.  That demands a significantly more robust infrastructure than just an EHR.

Medical devices must be fully integrated to provide up-to-the-second information on patient vitals to improve safety, while alerts and alarms have to be developed and presented to clinicians without hopelessly disrupting their workflows or annoying them into ignoring critical warnings.  Clinical decision support systems that support more accurate diagnoses and treatments must be able to draw on as much patient information as possible, even if that data is contributed through a health information exchange.

It is no wonder that providers are struggling to secure sufficient funding to invest in these myriad tools, and continue to face adoption and utilization challenges even if they do.  Despite a widespread interest in achieving the promises of predictive analytics, technological roadblocks and competing initiatives have been difficult to overcome while the technology market works to become sufficiently mature.

But those organizations that have cleared the initial hurdles of healthcare big data analytics are doing amazing things for patient care and their own financial health.  Predictive risk scores that help to prevent suicides, increase watchfulness in the ICU, aid surgeons in their decision-making, and even identify patients whose genes might betray them are becoming increasingly commonplace.

Advanced decision support from cognitive computing engines, natural language processing, and free-text analytics can help providers pinpoint diagnoses that might otherwise elude them, while population health management tools for providers and payers can highlight those most at risk of being readmitted to the hospital or developing costly chronic diseases.

Predictive analytics may be difficult, but healthcare organizations across the country aren’t letting that stop them from making significant progress with measurable impacts on the lives of patients.

Prescriptive analytics: Making the future work for you

The final phase of healthcare big data analytics involves obtaining prescriptive insights.  Prescriptive analytics moves beyond the ability just to predict an upcoming event and provides the capability to do something about it.

For example, if an organization is experiencing an inordinately high number of hospital-acquired infections, a prescriptive analytics program would not just flag the anomaly and highlight which patients in the ICU may be next on the list due to their vulnerable vitals, but would also automatically identify the particular nurse involved in the care of all these patients who may be spreading the infection and might need to be retrained about hand hygiene.  It may also help the hospital develop a more comprehensive antibiotic stewardship program to help prevent similar outbreaks in the future.

Prescriptive analytics doesn’t just predict what’s likely to happen, but actively suggests how organizations can best take action to avoid or mitigate a negative circumstance.  It requires such a seamless and completely integrated data analytics infrastructure that just a smattering of healthcare organizations have the capability to engage in this ultimate application of data in a large-scale or meaningful way.

But prescriptive analytics is the future of healthcare big data, and it’s on its way to becoming a reality.  As the Internet of Things creates a new way of looking at health information and machine learning advances and algorithms become almost unnervingly sophisticated in their ability to calculate the behaviors of nearly everything, from consumers choosing products at the grocery store to the minute movements of the stock market, the healthcare industry has an enormous opportunity to take advantage of these decision-making abilities.

The future of prescriptive analytics is nearly unlimited in its scope and depth as developers dream up the technologies of the future.  While too many healthcare providers are still trying to claw their way out of locked rooms of unusable EHR data, an industry-wide push towards viewing healthcare big data analytics as the answer to so many critical questions is accelerating the development of an infrastructure capable of becoming the foundation for prescriptive analytics and truly meaningful advances in the quality, timeliness, and effectiveness of patient care.

Better, Smarter, Healthier: In historic announcement, HHS sets clear goals and timeline for shifting Medicare reimbursements from volume to value

In a meeting with nearly two dozen leaders representing consumers, insurers, providers, and business leaders, Health and Human Services Secretary Sylvia M. Burwell today announced measurable goals and a timeline to move the Medicare program, and the health care system at large, toward paying providers based on the quality, rather than the quantity of care they give patients.

HHS has set a goal of tying 30 percent of traditional, or fee-for-service, Medicare payments to quality or value through alternative payment models, such as Accountable Care Organizations (ACOs) or bundled payment arrangements by the end of 2016, and tying 50 percent of payments to these models by the end of 2018.  HHS also set a goal of tying 85 percent of all traditional Medicare payments to quality or value by 2016 and 90 percent by 2018 through programs such as the Hospital Value Based Purchasing and the Hospital Readmissions Reduction Programs.  This is the first time in the history of the Medicare program that HHS has set explicit goals for alternative payment models and value-based payments.

To make these goals scalable beyond Medicare, Secretary Burwell also announced the creation of a Health Care Payment Learning and Action Network.  Through the Learning and Action Network, HHS will work with private payers, employers, consumers, providers, states and state Medicaid programs, and other partners to expand alternative payment models into their programs.  HHS will intensify its work with states and private payers to support adoption of alternative payments models through their own aligned work, sometimes even exceeding the goals set for Medicare.  The Network will hold its first meeting in March 2015, and more details will be announced in the near future.

“Whether you are a patient, a provider, a business, a health plan, or a taxpayer, it is in our common interest to build a health care system that delivers better care, spends health care dollars more wisely and results in healthier people.  Today’s announcement is about improving the quality of care we receive when we are sick, while at the same time spending our health care dollars more wisely,” Secretary Burwell said. “We believe these goals can drive transformative change, help us manage and track progress, and create accountability for measurable improvement.”

“We’re all partners in this effort focused on a shared goal. Ultimately, this is about improving the health of each person by making the best use of our resources for patient good. We’re on board, and we’re committed to changing how we pay for and deliver care to achieve better health,” Douglas E. Henley, M.D., executive vice president and chief executive officer of the American Academy of Family Physicians said.

“Advancing a patient-centered health system requires a fundamental transformation in how we pay for and deliver care. Today’s announcement by Secretary Burwell is a major step forward in achieving that goal,” AHIP President and CEO Karen Ignagni said. “Health plans have been on the forefront of implementing payment reforms in Medicare Advantage, Medicaid Managed Care, and in the commercial marketplace. We are excited to bring these experiences and innovations to this new collaboration.”

“Employers are increasingly taking steps to support the transition from payment based on volume to models of delivery and payment that promote value,” said Janet Marchibroda, Health Innovation Director and Executive Director of the CEO Council on Health and Innovation at the Bipartisan Policy Center. “There is considerable bipartisan support for moving away from fee for service toward alternative payment models that reward value, improve outcomes, and reduce costs. This transition requires action not only by the private sector, but also the public sector, which is why today’s announcement is significant.”

“Today’s announcement will be remembered as a pivotal and transformative moment in making our health care system more patient- and family-centered,” said Debra L. Ness, president of the National Partnership for Women & Families. “This kind of payment reform will drive fundamental changes in how care is delivered, making the health care system more responsive to those it serves and improving care coordination and communication among patients, families and providers. It will give patients and families the information, tools and supports they need to make better decisions, use their health care dollars wisely, and improve health outcomes.”

The Affordable Care Act created a number of new payment models that move the needle even further toward rewarding quality.  These models include ACOs, primary care medical homes, and new models of bundling payments for episodes of care.  In these alternative payment models, health care providers are accountable for the quality and cost of the care they deliver to patients. Providers have a financial incentive to coordinate care for their patients – who are therefore less likely to have duplicative or unnecessary x-rays, screenings and tests.  An ACO, for example, is a group of doctors, hospitals and health care providers that work together to provide higher-quality coordinated care to their patients, while helping to slow health care cost growth. In addition, through the widespread use of health information technology, the health care data needed to track these efforts is now available.

Many health care providers today receive a payment for each individual service, such as a physician visit, surgery, or blood test, and it does not matter whether these services help – or harm – the patient. In other words, providers are paid based on the volume of care, rather than the value of care provided to patients. Today’s announcement would continue the shift toward paying providers for what works – whether it is something as complex as preventing or treating disease, or something as straightforward as making sure a patient has time to ask questions.

In 2011, Medicare made almost no payments to providers through alternative payment models, but today such payments represent approximately 20 percent of Medicare payments. The goals announced today represent a 50 percent increase by 2016. To put this in perspective, in 2014, Medicare fee-for-service payments were $362 billion.

HHS has already seen promising results on cost savings with alternative payment models, with combined total program savings of $417 million to Medicare due to existing ACO programs – HHS expects these models to continue the unprecedented slowdown in health care spending.  Moreover, initiatives like the Partnership for Patients, ACOs, Quality Improvement Organizations, and others have helped reduce hospital readmissions in Medicare by nearly eight percent– translating into 150,000 fewer readmissions between January 2012 and December 2013 – and quality improvements have resulted in saving 50,000 lives and $12 billion in health spending from 2010 to 2013, according to preliminary estimates.

To read a new Perspectives piece in the New England Journal of Medicine from Secretary Burwell:

To read more about why this matters:

To read a fact sheet about the goals and Learning and Action Network:

To learn more about Better Care, Smarter Spending, and Healthier People:

Participants in today’s meeting include:

  • Kevin Cammarata, Executive Director, Benefits, Verizon
  • Christine Cassel, President and Chief Executive Officer, National Quality Forum
  • Tony Clapsis, Vice President, Caesars Entertainment Corporation
  • Jack Cochran, Executive Director, The Permanente Federation
  • Justine Handelman, Vice President Legislative and Regulatory Policy, Blue Cross Blue Shield Association
  • Pamela French, Vice President, Compensation and Benefits, The Boeing Company
  • Richard J. Gilfillan, President and CEO, Trinity Health
  • Douglas E. Henley, Executive Vice President and Chief Executive Officer, American Academy of Family Physicians
  • Karen Ignagni, President and Chief Executive Officer, America’s Health Insurance Plans
  • Jo Ann Jenkins, Chief Executive Officer, AARP
  • Mary  Langowski, Executive Vice President for Strategy, Policy, & Market Development, CVS Health
  • Stephen J. LeBlanc, Executive Vice President, Strategy and Network Relations, Dartmouth-Hitchcock
  • Janet M. Marchibroda, Executive Director, CEO Council on Health and Innovation, Bipartisan Policy Center
  • Patricia A. Maryland, President, Healthcare Operations and Chief Operating Officer, Ascension Health
  • Richard Migliori, Executive Vice President, Medical Affairs and Chief Medical Officer, UnitedHealth Group
  • Elizabeth Mitchell, President and Chief Executive Officer, Network for Regional Healthcare Improvement
  • Debra L. Ness, President, National Partnership for Women & Families
  • Samuel R. Nussbaum, Executive Vice President, Clinical Health Policy and Chief Medical Officer, Anthem, Inc.
  • Stephen Ondra, Senior Vice President and Chief Medical Officer, Health Care Service Corporation
  • Andrew D. Racine, Senior Vice President and Chief Medical Officer, Montefiore Medical Center
  • Jaewon Ryu, Segment Vice President and President of Integrated Care Delivery, Humana Inc.
  • Fran S. Soistman, Executive Vice President, Government Services, Aetna Inc.
  • Maureen Swick, Representative, American Hospital Association
  • Robert M. Wah, President, American Medical Association