Amstat Analytics Group has become nationally recognized for helping hospitals chart their new course with greater efficiency and agility. From implementation to data migration to tuning and optimization to advanced analytics, the Amstat Analytics Group Professional Services team will work with you every step of the way. Our clients cite these reasons for choosing to work with us:
- All of our principals have doctorates at leading universities including Harvard, Stanford, and Columbia.
- Amstat Analytics Group has numerous healthcare associates across multiple locations with proven domain competence.
- The team includes doctors, clinical specialists, statisticians, and data scientists.
- We have extensive backgrounds in healthcare analytics and over 100 years of practical experience in the healthcare field.
- We have more than 650 skilled resources dedicated to healthcare research, reporting, and analytics practice.
- We bring our cumulative experience working with close to 900 hospitals on revenue cycle issues. You benefit from your peers’ successes solving the same problems you face today.
- Our consultants work closely with your staff so they have the skills and tools to keep improving performance long after we’re gone.
- Our recommendations are based on more than 100 years of best practice research on hospital management, including intensive research into techniques to optimize patient access processes.
- We can help providers operate their revenue cycle optimally.
- We are able to develop, educate, and implement the best solution for your facility or medical practice.
- We work with management to develop a strategy and a team to implement “best practices” and maintain optimization.
The problem: Hospitals are experiencing a number of problems. Those include lower reimbursement resulting in margin compression, disparate data systems that result in inconsistent, expensive data and metrics, and data that are updated weekly, monthly or quarterly, which results in a lack of any actionable workflow.
Solution: Among the most effective emerging tools for healthcare providers to streamline and optimize processes while maximizing revenue is predictive analytics. A majority (93%) of healthcare payers and providers believe predictive analytics is important for the future of their business, according to a 2017 Predictive Analytics in Healthcare Trend Forecast by the Society of Actuaries. Hospital revenue cycle management experts promote medical predictive analytics as the newest solution to translating massive loads of health data into actionable insight to improve financial performance.
Predictive analytics has been successfully deployed across nearly every industry and sector of the US economy. The ability to identify the likelihood of future outcomes based on historical data is around us every day—think Amazon and Netflix recommendations—but have you ever wondered what it can do for your organization?
Every hospital has amassed large data assets that characterize their interactions with patients, physicians, and payers, among others. We’ll zero in on the process and work needed so that a hospital can tap into those data assets to operate more efficiently from a revenue cycle perspective.
Accomplishing this requires connecting data assets with technology – specifically predictive analytics, data warehousing and reporting. Implementing predictive analytics can enable a healthcare organization to improve processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks.
We apply this data in ways that improve financial performance, similar to how other industries have been doing for decades. We can help leaders make smarter decisions. We can:
- Implement predictive analytics to enable you to improve revenue cycle processes and payment collection opportunities, create consistent and measurable metrics across the revenue cycle, and manage staff resources in a way that streamlines workflow and reduces unessential tasks
- Use predictive models to figure out if a charge is missing; a patient will pay his or her part of the bill; a patient will be readmitted, or a diagnosis-related group code was assigned in error
- Leverage predictive analytics to look back at a case or issue and see how it was handled
- Use predictive analytics to achieve greater revenue cycle efficiency and better overall resource allocation
- Predict issues that may come up in the future
- Use algorithms to examine historical data and make correlations
- Predict not only if a patient is likely to pay, but also how much he or she is likely to pay
- Create self-pay predictive models that include a number of criteria, such as self-pay type, patient class (whether outpatient or inpatient), payment history, and debt history
- Analyze the company’s payment habits, provide the probability that the company will pay a trade account in a “severely delinquent” manner (90+ days beyond terms) within the next 12 months through the Dynamic Delinquency Score (DDS), and include comparisons to other companies within the same industry
- Answer even more
- Is there a deterioration in payment habits?
- Will they pay me on time?
- How much credit do suppliers usually extend to them?
- Explore the hidden opportunity in hospital charging data—missing charges, undercoding, and meaningful coding anomalies
- Use predictive analytics and data mining to uncover these hidden pockets of opportunity
- Analyze the real-world impact on revenue integrity and charge-capture KPIs
- Leverage predictive analytics to deliver improved financial performance
- Use predictive analytics for audits to identify missing revenue
- Review the charges on the claim with an application that can predict errors
- Help you source the root cause of errors
- Identify drivers behind patient readmission
- Identify the normal revenue-cycle processes, model them, and then pinpoint the cases that exhibit anomalies
- Use algorithms to examine historical data and make correlations
- Identify trends by drilling down to the staff, department, and service levels to uncover insightful details
- Help you gain insight into how to more effectively invest time and efforts
Result: The result should be a system that focuses the right staff on the right accounts at the right time to maximize return and efficiency. Ultimately it should allow the revenue cycle staff to work smarter.
Applying predictive analytics to your business processes will ultimately yield both quantitative and qualitative results. The quantitative results are easily measured by increased point-of-service collections, a reduction in bad debt and the cost to collect, all while maintaining or lowering operating expenses. The qualitative results come in the form of employee and patient satisfaction.
Information empowers employees to work smarter, not harder. Many times front-line employees are asked to use a one-size-fits-all approach to processing patient accounts because they have no insight into the patient’s ability to pay. Consumer credit data can provide employees with a clear indication of a patient’s capacity to pay, therefore allowing the accounts to be processed in a more efficient manner.
Moreover, predictive analytics allows hospital employees to prioritize actions with respect to open account balances by helping to identify the accounts with the highest likelihood of collections. The result is an increase in productivity, an improvement in financial results and satisfied employees.
The problem: The challenge of bundled payments and re-admission rates is increasing billing complexity and denials. Underpayment has become so commonplace it is assumed. Denial patterns have become increasingly impenetrable. ICD-10 is poised to usher in an entirely new class of billing mistakes. In this environment, you are being judged by your ability to recover revenue and accelerate cash flow.
As a revenue cycle leader, you want to gauge the denial risk of an account before the claim leaves your organization. You want denial issues and trends identified by payer, at the plan and service level to give pointed direction as to where the originating denial issues lie. You want to know about the hidden denial patterns that are attributing to your net revenue leakage. You want your revenue analyst team working in just that capacity, and getting away from simply preparing reports. Finally, you want to view commonalities among your peers with respect to denial patterns and see where your organization stands out from the rest of the pack.
Solution: Amstat Analytics Group will give you the strategic lens that is sorely needed when it comes to denials avoidance. We will provide your team with specific areas of revenue-risk to focus efforts to correct the originating issue(s). We complement your current denials management and workflow systems as we examine initial denial issues based on predictive modeling. Predictive modeling can be used to estimate the likelihood of a claim denial prior to submission. This allows staff to focus only on those claims likely to be denied – based on past denial patterns – in order to minimize delays in reimbursement.
We will provide you with a comparative analysis of initial denial rate at the peer group and payer level.
- Leverage predictive analytics to predict where denials are likely to occur next and drive the operational change needed to reduce denial rates. This allows staff to focus only on “those claims likely to be denied – based on past denial patterns – in order to minimize delays in reimbursement.
- Understand what non-obvious factors are correlated and contributing to your initial denial rate
- Get denial overturn rates with associated net revenue
- Help you redesign your front office to maximize point-of-service collections, optimize financial counseling, and improve customer service
- Help you optimize your registration processes to simplify the medical necessity and authorization and reduce the likelihood of downstream denials
- Benchmark on a payer-to-payer and peer-to-peer basis
- Aggregate disparate data streams into one analytic application that delivers the revenue cycle decision support needed in today’s climate
- Audit your final denial rate
- Store your claim and remittance data within one application
- Address clinical and operational back office issues that are causing payment denials and delays
- Provide comparative analysis and benchmarking that scores payer performance based on claim, rejections, denials, and exceptions
The problem: In today’s evolving healthcare climate, hospitals and health systems are challenged to provide higher-quality care while operating on leaner budgets. Namely, the shift away from fee-for-service to fee-for-value aims to make higher-quality, lower-cost care more accessible to patients. New reimbursement models, in addition to growing consumerism and competition, further emphasize the need to recover and maximize every dollar.
Solution: Big data and applied analytics can help organizations address these challenges, offering insights into the revenue cycle that otherwise would be difficult to attain at such a granular level. Hospitals and health systems that use strong data analytics service have the potential to measure, compare, and learn from nearly every element in the revenue cycle.
- Help you make better decisions by leveraging data and analytics in revenue cycle management
- Provide you with specific areas of revenue-risk to focus efforts to correct the originating issue(s)
- Provide data and analytic support for administrators regarding decision making about program and service performance
- Improve your workflows, operational performance, and financial results by leveraging your data across the revenue cycle, matching it, and analyzing the account across the various revenue cycle workflows and transactions
- Ensure accurate reimbursement by analyzing workﬂows and optimizing activities
- Create and monitor revenue cycle KPIs around pre-service, point-of-service, post service, and denials to provide data points needed for process and financial optimization
- Maximize return on investment
- Help staff aware of the issues that truly require investigation, action, and resolution
- Enable the calculations of HFMA Map Keys and NAHAM Access keys for true peer-to-peer benchmarking
- Help you build a system that focuses the right staff on the right accounts at the right time to maximize return and efficiency
- Help you reduce manual tasks, and increase productivity
- Compare revenue and collection performance to your peers
- Help providers achieve an optimized revenue cycle process through an integrated approach, rather than a series of unconnected steps
The problem: It is a common theme in our discussions with CFOs and CEOs across the country: healthcare is moving from fee-for-service, volume-based reimbursement to quality-based reimbursement. The days of higher volumes equating to more revenue will soon be fully in our rear-view mirror. This change may already be having a huge impact on your reimbursement. Healthcare executives across the country often ask us: How can I ensure that quality-based reimbursement does not send my hospital’s finances down the drain?
Solution: expand the role of clinical documentation improvement (CDI) to review records for documentation that impacts quality measures. By doing this, you will make sure that you get paid the right amount for the high-quality care your hospital provides and not get dinged for quality scores that do not reflect reality. We can:
- Help you get the most out of your CDI program
- Help your staff take care of your CDI program effectively, optimize processes, and strengthen interdepartmental relationships—whether you already have a clinical documentation improvement program in place or not
- Analyze clinical and financial data and review charts to uncover your biggest opportunities to boot revenue and enhance quality scores
- Analyze which physicians have the most opportunity to improve their documentation
- Assess your preparedness for ICD-10 and create a customized implementation blueprint
Dr. Raj Singhal, MD., Director, Pediatric Anesthesiology, Phoenix Children’s Hospital
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