CASE STUDY
Streamlined Claim Reimbursement
We enabled our client to maximize insurance reimbursements by deploying AI-powered billing code selection.
Frontline caregivers often have only a few minutes between patient visits – yet within that time, they must complete critical administrative tasks that enable hospitals to receive payment.
While the specifics of this process vary by institution, it generally involves submitting detailed claims to insurers that require outlining services rendered and associated costs. Although much of the process has been digitized, in many instances caregivers are still required to manually identify and input treatment codes from extensive databases or digital catalogs, leading to frequent errors.
According to industry sources such as Change Healthcare and the American Medical Association (AMA), coding errors are responsible for approximately 15% to 25% of all claim denials. Our client experienced a significant impact to their operating capital as a result of these denials, prompting them to engage Fei Business Advisory for strategic guidance and technical support.
In under six months, our team designed and deployed an AI-powered solution that pre-selected billing codes specific to the patient treatment, insurance provider, and other PII information. The system helped reduce billing code errors by 75% and increase operating capital to more sustainable levels for our client.
In addition, the solution enabled frontline caregivers to quickly and accurately identify treatment codes using a voice-forward interface, optimized for their fleet of mobile devices. Caregivers were now able to select billing codes from a pre-filtered list, reducing the time requirements for these tasks and allowing them to focus on patient care.
Fewer coding errors
Enabling caregivers to use a voice-forward interface to identify treatment codes improved billing accuracy and reduced claim denials.
Reduced claim denials
Correcting errors led to recovering millions in working capital, saving our client time and effort reworking claims.
Greater caregiver efficiency
Allowing caregivers to complete administrative tasks quickly improved the overall patient experience.
CASE STUDY
Programmatic Contract Assessments
We reduced legal costs and contract review timelines for onboarding new vendors.
Effective third-party management programs encompass an end-to-end, integrated approach to assess risk during the contract process. However, skillset limitations or workforce shortages frequently require the use of third-party legal counsel to review new or amended agreements, driving up costs and increasing timelines. Fei Business Advisory, Inc. helped an eCommerce retailer minimize these pain-points, increase assortment, and boost revenue for a specific product category.
The approach began with engaging several client Procurement Teams to help identify costly bottlenecks associated with the legal review process. By collaborating with in-house legal departments, category managers, suppliers, and product managers, Fei Business Advisory, Inc. identified several tactical issues with the workflow and proposed a custom solution that sent only high-risk contracts to the legal team for review.
The solution leveraged an open-source LLM that we fine-tuned on existing contracts, legal templates/boilerplates, and company policies. We then created a data pipeline and micro service to send any uploaded vendor documents to the LLM and scan for adverse clauses. Documents that had a large number of adverse clauses got sent to the legal department for a secondary screening and documents that didn’t were entrusted to line managers for rework.
The results from this solution reduced time to contract by over 30% for the client and allowed the legal team to terminate their costly engagement with the third-party law firm. In addition, the client Procurement Department was able to move faster, increasing the product assortment and rev-share revenue by 10% over the same period, resulting in greater customer satisfaction and overall company growth.
Prevent damages
Our solution helped in-house legal teams hone in on contracts with adverse clauses that prevented millions in damages.
Reduce legal bottlenecks
The impact from our risk framework decreased legal fees and time to contract.
Focus on top-line growth
Semi-automating contract review allows Procurement Teams to focus on high value add product and service assortment.
CASE STUDy
Clinical Workforce Optimization
We supported a large west coast hospital system reimagine workforce capacity to improve patient experience and allow caregivers to focus on higher-level responsibilities.
Staffing Coordinators are responsible for caregiver and practitioner work schedules at hospitals. The accuracy of their work is crucial for smooth operations. However, many hospitals have several systems for creating schedules, adding complexity and friction to the staffing process. Furthermore, lack of integration among these systems requires Staffing Coordinators to potentially update multiple interfaces with pieces of the same information and manually check for cohesion and accuracy.
Staffing errors are critical issues for hospital systems. The impact they have on operations result in slower patient turnover and inconsistent levels of service, which lowers overall patient satisfaction scores. They also impact caregivers, frequently extending work shifts, resulting in higher caregiver turnover, burnout, and potentially, malpractice.
Fei Business Advisory, Inc. supported a large West Coast hospital system address many of these challenges. In partnership with Staffing Coordinators, front-line caregivers, and Human Resources professionals, we outlined a product and data strategy to reduce the complexity of staffing workflows by integrating data sources and using AI to help optimize timetables.
We executed on the vision by polling front-line caregivers to identify variables that impact staffing. Using open-source products, we assigned weights to each of these variables by looking at periods of historic under and over staffing. Finally, we optimized an LLM to use these weights when selecting staff and programmed constraints around the number of caregiver hand-offs, maximum shift length, and specialty presence.
After a 3mo testing phase, preliminary results suggested that our system predicted staffing shortages with 91% accuracy. We used the data to extrapolate impact to caregivers and hospital operations, resulting in significantly lower extended work shifts and an increase in patient satisfaction scores. The engagement resulted in contract extension to implement a production ready version over the following year.
Predict staff shortages
Our solution predicted staffing shortages with 91% accuracy, enabling our client to meet additional demand at lower hourly rates.
Decrease long work shifts
The impact from better prediction helped reduce extended works shifts for front line caregivers.
Increase patient satisfaction
Patient satisfaction scores increased thanks to higher patient turnover and consistent caregiver assignments.