Unified Database and Insurance Claim Outcome Algorithm

Despite substantial progress over the past years, insurance claim denials remain a topical issue for both patients and healthcare institutions. As for the former, this problem entails the lack of financial security and the potential inability to receive proper treatment. As for health service providers, insurance claim denials account for a significant portion of monetary losses. Modern technology allows for creating an advanced evidence-based algorithm, which would use the existing precedent data and calculate the most efficient utilization of resources while minimizing the possibility of denial. The purpose of this paper is to explain the necessity of such an algorithm in the 21st century environment.

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Generally, my duties as a surgery denials prevention coordinator include the analysis of all scheduled surgeries in terms of resource utilization. This work aims at estimating the procedures’ efficiency in the context of future reimbursement. Nevertheless, denials remain a topical problem despite all efforts made recently. Reiner (2018) states that out of three trillion dollars in total, two hundred and sixty-two billion dollars of insurance claims were denied. This resulted in an average loss of five million dollars per healthcare provider nationwide. A more recent study by Matson et al. (2020) indicated a denial rate of 19.3%. The lowest number was in private insurance, whereas Medicare statistics showed the highest rate. Kovach and Borikar (2018) state that insurance claim denials account for 3-5% of the annual health system revenue losses. In addition, filing an appeal could entail further expenses, thus, putting extra stress on healthcare institutions’ financial aspects.

Denials prevention coordinators work on alleviating this issue in an array of institutions throughout the country, but, as of now, there is a lack of coordination. As the 21st century demonstrates substantial progress in informational technology development and implementation, the progress can be used in this sphere as well. Modern databases are capable of storing and calculating considerable amount of information, and the creation of such a unified insurance claim database would be a crucial step in alleviating the issue in question. It will encompass all the participating health service providers and contain the primary information concerning the insurance claim details, including the patient’s background, treatment history, and particularities. Accordingly, the database will have a large sample of practical cases. The next step will be to introduce an instrument to process the information and provide an in-depth analysis in relation to new cases.

The instrument mentioned above is an algorithm that will calculate the likely outcome of an insurance claim based on the provided data. In other words, the program will study each case in terms of its similarity to existing precedents. The algorithm will calculate the outcome using the variables, which are the planned and completed clinical procedures, the patient’s background, and other pertinent details. As far as the second group of variables is concerned, it is implied that an evidence-based approach should be used to increase the algorithm’s accuracy. This group will include a range of personal variables, such as age, gender, social status, occupation, and ethnicity. Evidently, the algorithm is to be based on the denials prevention coordinators’ professional expertise, which is especially important during the first stages of its development and implementation. However, the algorithm is expected not to replace the specialists but to enhance their professional capabilities.

The proposal discussed in this paper aims at reducing the denial rate, which would be helpful to both patients and healthcare institutions. Essentially, there are several social groups, which report higher rates of insurance claim denial, and, as a result, such individuals may be refused quality treatment moving forward. Kattari et al. (2020) conducted relevant research and concluded that transgender and non-binary people are more likely to have their insurance claim rejected. Consequently, they are often denied transgender-specific procedures, and this tendency contradicts the essential values of inclusiveness and equality. Moreover, despite The Affordable Care Act guidelines, Mackay et al. (2017) report that the insurance denial rate is considerable among cancer patients. They suggest that this situation impedes the development of cancer research and proves to be inconsistent with the federal government initiative. If an insurance claim result algorithm is devised and implemented, it will contribute to the denial rate decrease and provide vulnerable social groups with required medical services.

The development of the database and the algorithm would require significant interprofessional communication. First, the effort must be made by the healthcare professional groups. Their combined experience will provide an objective insight into the program’s efficiency to be beneficial to all parties involved. Additionally, such project will require that experts in informational technologies and data analysis take part in it. While the proposal aims at improving the quality of health services, such specialists have experience in organizing similar networks in other spheres, which is why their contribution will be as essential as healthcare professionals’ will.

As for the program’s funding, there are several sources that can be utilized. The best way of doing so will be through collaboration with other healthcare institutions as this project aims at improving the sphere in general, and broader cooperation will make it more efficient. In addition, the program will tackle the general issue that the nation faces, which is why it can cause the situation when governmental funds might be allocated to it in case a corresponding request is made. Accordingly, the project’s results will be evaluated in terms of its financial outcome since its primary purpose is to reduce health services’ revenue losses due to insurance claim denials.

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Overall, the discussed initiative can be easily implemented in a short period, as the system already possesses the required resources. The proposed database will unify the existing data, and the algorithm will translate the individual expertise of each denials prevention coordinator into the domain of computer data analysis. Similar networks are used in a variety of spheres and particular businesses, bringing positive results in terms of improving their financial performance. Undoubtedly, this initiative can become a valuable contribution to the system and prove to be beneficial to both medical practitioners and the public.

References

Kattari, S. H., Bakko, M., Hecht, H. K., & Kinney, M. K. (2020). Intersecting experiences of healthcare denials among transgender and nonbinary patients. American Journal of Preventive Medicine, 58(4), 506-513. Web.

Kovach, J., & Borikar, S. (2018). Enhancing financial performance: An application of lean six sigma to reduce insurance claim denials. Quality Management in Health Care, 27(3), 165-171. Web.

Mackay, C. D., Antonelli, K. R., Bruinooge, S. S., Saint Onge, J. M., & Ellis, S. D. (2017). Insurance denials for cancer clinical trial participation after the Affordable Care Act mandate. Cancer, 123(15), 2893-2900. Web.

Matson, A. P.., Earp, B. E., Benavent, K. A., Geresy, K., Collins, J. E., & Blazar, P. E. (2020). Predictors of insurance claim rejection in hand and upper extremity surgery. Journal of the American Academy of Orthopaedic Surgeons, 28(15), e662-e669. Web.

Reiner, G. (2018). Success in proactive denials management and prevention: Tackling the causes of claim denials from the front end can help healthcare organizations reduce denials and increase the success rate of claims appeals. Healthcare Financial Management, 72(9), 52-58.

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