DHA 8007 Week 6 Assignment Accounts Receivable Management

Accounts Receivable Management

The analysis of days in accounts receivable (AR) is essential for assessing the financial performance of the healthcare setting. In the context of the Westside Clinic, the AR management took around 58 days in 2022 compared to 59.2 days in 2021, showing improved performance. The AR management showed a positive trend in 2022, but there is a need for more reductions in the days to improve the cash flow. Financial leaders must understand the analysis of the AR to maintain normal operations in healthcare settings (Antysheva et al., 2020). In the assessment, comprehensive insights into the strategic assessment, long-term financial trends, and risks for inadequate AR management are provided. Additionally, the trends affecting the revenue cycle and impact on organization strategy, patient outcomes, and community health are also provided.

Strategic Assessment

The financial analysis of the AR helped identify the average days required to collect the receivables for healthcare services. Analysis of the data for the 2021-2022 year showed that AR days reduced from 59.2 to 58, improving the cash flow and financial stability of the organization. The strategic assessment focuses on the three ways to reduce the AR days. The three ways to reduce the AR days are technology usage, denial management system, and predictive analytics. Firstly, artificial intelligence (AI) technology was introduced to automate billing and collection processes (Nasution et al., 2020). Applying a machine learning algorithm for the financial analysis will likely shorten the process timing and faster payments, lowering the AR days.

Secondly, denial management processes helped minimize the likelihood of claim denials and rejections (Chaudhuri et al., 2022). A pre-investigation process before submitting claims ensures that no erroneous claims are made which will lead to denial. Improving the claim settlement reduces the claim reprocessing, reducing the AR cycle time. Thirdly, predictive analytics is used during the revenue management cycle (RCM) to identify potential payment problems to prevent AR delays (Chaudhuri et al., 2022). Using the analytics helped in predicting future payments into the RCM system. Collecting data to assess the ratio of the patients who received care helped in accessing the revenue and facilitated the anticipative decision, leading to a reduction in the accumulation of AR in healthcare settings.

Plan for Managing Risk

The risks of the organizational environment when collecting AR receivables include cash flow disruptions and increased bad debt. Longer AR days lock up working capital that can be used to fund operations or make other fruitful investments. Prolonging of receivables for more than 120 days affects the profitability of an organization (Laghari & Ahmed, 2023). Firstly, the verification system will be implemented to verify the patient having insurance before the healthcare services, Pre-verification process helped in reducing claim denials. Secondly, the analysis of the denial will be conducted monthly to assess the common problems such as coding mistakes or lack of information. Thirdly, the AI technology will be used to submit claims automatically, track claim status online, and receive updates on receivables. The technology minimizes the possibility of human mistakes and offers vital information for optimizing collection methods and financial predictions. Additionally, enhancing patient knowledge will also reduce payment issues, which is important for a timely collection of the AR and overcoming delays.

Strategic Assessment to Monitor Days in AR

Monitoring days in AR is necessary to streamline the cash flows and financial performance of the organization. In Westside Clinic, the monitoring of AR days is important to minimize delays in collection, loss in revenues, and overall financial stability of the organization. The AR days is a metric that reflects the number of days a healthcare organization takes to convert billed amounts into cash (Antysheva et al., 2020). Therefore, assessing the monitoring days in AR is vital as shorter AR days mean faster collection, which helps the hospital control cash flow effectively.

The three ways that can be used by healthcare providers to monitor the AR days include AR tracking tools, key performance indicators (KPIs), and predictive analytics. Firstly, AR tracking tools work as financial software that has interface dashboards showing days in AR, time taken for the AR, and payer performance (Arora et al., 2024). The notifications in the tracking tools helped identify the cases in which the AR days go beyond the deadline. Therefore, real-time monitoring tools help the staff to address AR issues proactively, increasing the collection efficiency.

Secondly, KPIs help the healthcare provider to set the key objectives for AR management. Comparing the hospital data related to the AR days with national and regional benchmarks helped in identifying the days used for AR clearance (Komarraju et al., 2024). Different KPIs include the percentage of the claim paid within the first thirty days and the percentage of receivables older than ninety days. Thirdly, predictive analytics will be used to identify the possible delays in payments (Antysheva et al., 2020). The AR days can be decreased by using predictive analytics to find out which accounts are likely to be challenging for payment clearance. Integrating predictive analytics into the RCM helps the healthcare provider monitor the average days used for AR in healthcare settings.

Pros and Cons

The pros of the implementation of AR analytic tools, KPIs, and predictive analytics include a reduction in the AR days by improving the cash flow. Tracking tools eliminate manual work while real-time dashboards fasten the decision-making process for AR management (Arora et al., 2024). Additionally, automated alerts are useful for timely follow-ups on cases that require claims. The KPIs offer specific goals and facilitate the clinic’s control over performance and alignment with benchmarks. Setting up standards also helps healthcare providers to achieve the set targets for AR performance. Analytical tools give information on payment patterns and help to address issues regarding AR, facilitating the strategic decision-making process in healthcare settings (Antysheva et al., 2020).

The cons related to the implementation of measures include high initial capital expenditure and ongoing costs for maintenance and buying accessories (Wassie, 2021). New systems can also require extensive staff training to use the tracking tools and predictive analytics competitively, increasing the upfront costs. Additionally, healthcare providers also experience obstacles and delays during the integration of the new devices with the already existing financial platforms (Yeboah, 2024). On the one hand, overemphasis on KPI targets can create pressure that might lower morale or encourage taking shortcuts in handling claims.

Long-Term Financial Trends for AR Management

The automation of AR management helps in improving the claim processing system and reduces the risk of manual errors. Automation increases effectiveness, decreases expenditures, and lowers the AR cycle (Wassie, 2021). Another benefit of predictive analytics is the ability to anticipate payment trends so that healthcare providers can manage receivables in a timely manner. Implementation costs are high, and regular updates and training of staff can be costly to practice, especially those with few staff. However, the long-term perspectives stated that healthcare providers need to embrace technology to improve AR management in healthcare settings (Nasution et al., 2020).

Another financial trend is the transformation of the care delivery process. The healthcare industry changed the care delivery model from the fee-for-service model to value-based care. The payment structures linked to value-based care include performance incentives. The successful examples of financial incentives in value-based care include decreased rates of rehospitalization and lowered healthcare utilization (Beauvais et al., 2022).

Healthcare providers have to align AR strategies with value-based contracts to sustain the financial stability of the organization. The increase in patient responsibility for managing medical expenses through high deductibles and co-payments is another trend. The trend indicates that a percentage of receivables is focused on patient payments. Implementing patient engagement in the medical payment process through various methods has an enhanced cash flow and fewer bad debts (Matolo, 2023). Higher patient responsibility means a higher risk for bad debt and extended AR, which means that strong billing tactics and patient financial literacy campaigns are necessary. Enhancing the ability to interact with patients on financial matters is essential for AR management.

Analyzing Risks Surrounding Inadequate AR Management

The AR management is of utmost importance because mismanagement may lead to negative consequences such as cash flow disruptions, bad debt, and resource drainage. Poor AR management means delayed cash collection that adversely affects cash inflow (Laghari & Ahmed, 2023). The delayed payments from insurers can lead to cash flow challenges, where the healthcare institution may not be in a position to meet operational costs.

Furthermore, the cash flow disruptions reveal the need for more funds to cover any deficits, which unfortunately raises the cost of capital and the risk profile of the organization. Another risk associated with the poor management of AR is bad debt (Kramoliš & Dobeš, 2020). In particular, bad debts that take more than 120 days to collect become even more impossible to recover. Extended collection periods of AR result in increased bad debt, affecting the organization’s performance (Kramoliš & Dobeš, 2020). In terms of profit margins, the direct correlation to the increase in bad debts affects the reinvestment of funds, which affects the organization’s performance and productivity.

Inadequate management of AR also led to resource drainage as the increased manual work increased the risk of claims. Failure to effectively manage AR entails a lot of extra work and increases the risk of denied claims. The claim needs extra time and manpower, which affects the resources. Loss of efficiency due to poor AR management results in the decline of total organizational efficiency (Hanifah & Bastian, 2020). Additionally, the poor AR management also delayed the collection times, which affected the operational efficiency of the organization due to the resource drainage. Moreover, the lack of resources due to inadequate AR management hinders the provision of quality care, lowering patient satisfaction.

Impact of Significant Change on Revenue Cycle

The revenue cycle is defined as the process of patient payment in a healthcare setting, from the time a patient billing process to the collection of payment. Emerging trends in the healthcare industry helped in managing the revenue cycle management (Chaturvedi & Sharma, 2024). The three significant trends that impact the revenue cycle include patient financial responsibility, value-based care, and AI technology for AR management.

AI Technology

The AI technology plays a crucial role in the process of AR management by facilitating the RCM in healthcare settings. Refurbishing the RCM process by using AI and machine learning algorithms decreases the number of administrative tasks, avoids errors, and increases operational efficiency (Ali & Dzandu, 2023). The AI technology helped organizations manage the claims, denial, or payment reconciliation process without human resources, lowering manual errors. Enhancements enable healthcare providers to manage AR delays, improving cash management. Moreover, the AI also can prevent claim denial by identifying coding or documentation mistakes (Pennington & Coustasse, 2024). Data processing solutions provide data on the financial status of an organization and help providers manage AR more effectively through strategic measures.

Value-Based Care Model

The value-based care model transformed the healthcare delivery and payment process. Services provided under the value-based care model helped organizations to receive payment for the services offered instead of the quantity of service. Value-based care highlights the efficiency of healthcare service delivery through patient satisfaction (Pounds, 2021). Additionally, value-based care includes payment per procedure or treatment that streamlines the financial process. For instance, if the patient received two services the payment will be doubled without any argument. Value-based care means providers are paid depending on their performance and thus they are likely to incur higher costs (Cattel & Eijkenaar, 2019). However, monitoring and recording of outcomes in patients can affect the billing and reimbursement, affecting the organization’s performance.

Patient Financial Responsibility

The increased health insurance costs and higher out-of-pocket costs made the patient responsible for the financial process. Patients are bearing a higher proportion of healthcare costs than before through either copayment, coinsurance, or deductible. As the patients are required to contribute more towards hospital bills, the providers are experiencing a reduction in the AR days (Atluri & Thummisetti, 2023). However, few patients are unable to afford to make large payments at once which leads to a long payment period. Healthcare organizations require adequate tools for handling payment structures (Rebuin et al., 2023). Additionally, the healthcare organization also guides the financial assistance policies to improve the receivable process.

Impact of Significant Trends

The three trends such as AI technology, value-based care, and patient financial responsibility influence organizational strategy, patient care, and community health. Assessing the impact of trends helped in identifying the importance of AR management in healthcare. The AI technology trend indicated that automation helped in managing coding errors and improving organizational performance (Ali & Dzandu, 2023). Value-based bare helped in managing the billing process and enhancing patient safety. Patient financial responsibility also helped in the timely management of payment, improving the organization’s performance.

Impact on Organizational Strategy

The AI technology improves the AR management process in the healthcare setting use of automated tools for claim management, follow-up to payments, and denials will lower the chances of manual errors, enhancing collection efficacy. Additionally, AI technology strengthened financial stability by reducing the days in AR and improving the cash flow (Ali & Dzandu, 2023). Additionally, value-based care focuses on the quantity of care that helped in adopting new AR management approaches for reimbursement. Improved cash flow and reduced days in AR will lead to stronger financial stability. With faster collections, the clinic will have more predictable revenue, enabling more accurate budgeting and strategic decision-making (Cattel & Eijkenaar, 2019). The increase in patient financial responsibility due to the high deductible and copayment also enhanced the organization’s performance. Patients ensure to pay their bills timely which leads to predictable revenue, enhancing the organization’s performance.

Impact on Patient Care

The AI technology reduces billing and claims processing time spent by the healthcare staff which helps in providing more effective care to the patient. Through the use of automated billing solutions, patients will get accurate billing details that enhance satisfaction and trust (Ali & Dzandu, 2023). Additionally, value-based care also fosters patient trust and satisfaction by providing effective services. Value-based care focuses on improving the patient experience, leading to better health outcomes. Moreover, the patient’s financial responsibility also streamlined the payment process (Atluri & Thummisetti, 2023). Easier billing and payment methods are beneficial for the patients and improve the patient’s trust and satisfaction with the services.

Impact on Community Health

Trends also impact community health drastically. Firstly, the AI technology improved the AR management process which facilitated the hospital to invest more in patient care services, increasing the health care availability to the community. Moreover, the improved cash flow and reduction in debt also helped in conducting outreach programs, improving community health (Ali & Dzandu, 2023). Likewise, the value-based model also ensures the provision of effective and high-quality services to the community. The value-based care also focuses on the prevention of disease, early detection, and general management of chronic conditions, improving community health (Pounds, 2021). Additionally, the patient’s financial responsibility also ensures that equitable care services are provided to the community, improving health outcomes.

Conclusion

The strategic assessment for AR management is essential for assessing the financial performance of the healthcare setting. Additionally, monitoring days in AR is necessary to streamline the cash flows and financial performance of the organization. The long-term financial trends provided insights into how AR management affects the financial stability of the healthcare organization. Inadequate AR management leads to potential risks such as cash flow disruptions, bad debt, and resource drainage. The three identified trends are patient financial responsibility, value-based care, and AI technology that impact the RCM in healthcare organizations. Additionally, the trends also impact patient health, organization strategy, and community health.

References

Ali, N., & Dzandu, M. D. (2023). A paradigm shift for medical health care to focus on a service-value approach to achieve greater patient satisfaction. Journal of Health Organization and Management. https://doi.org/10.1108/jhom-06-2022-0180

Antysheva, E., Prishutova, T., & Frolova, V. (2020). Accounts receivable management in digital transformation. Proceedings of the International Scientific Conference – Digital Transformation on Manufacturing, Infrastructure and Service. https://doi.org/10.1145/3446434.3446486

Arora, P., Desu, L., Kumar, A., Kumar, S., & Marinescu, A. (2024). Enhancing profitability through AI-optimized accounts receivable: reducing cash conversion cycles. Institute of Electrical and Electronics Engineers Xplore, 1–5. https://doi.org/10.1109/icecet61485.2024.10698580

Atluri, H., & Thummisetti, B. S. P. (2023). Optimizing revenue cycle management in healthcare: A comprehensive analysis of the charge navigator system. International Numeric Journal of Machine Learning and Robots, 7(7), 1–13. https://injmr.com/index.php/fewfewf/article/view/37

Beauvais, B., Whitaker, Z., Kim, F., & Anderson, B. (2022). Is the hospital value-based purchasing program associated with reduced hospital readmissions? Journal of Multidisciplinary Healthcare, 15, 1089–1099. https://doi.org/10.2147/jmdh.s358733

Cattel, D., & Eijkenaar, F. (2019). Value-based provider payment initiatives combining global payments with explicit quality incentives: A systematic review. Medical Care Research and Review, 77(6). https://doi.org/10.1177/1077558719856775

Chaturvedi, R., & Sharma, S. (2024). Implementing predictive analytics for proactive revenue cycle management. Journal for Research in Applied Sciences and Biotechnology, 3(4), 74–78. https://doi.org/10.55544/jrasb.3.4.9

Chaudhuri, R., Parsa, S. P. K., Nagpal, D., & R, K. (2022). Time to response prediction for following up on account receivables in healthcare revenue cycle management. Communications in Computer and Information Science, 124–137. https://doi.org/10.1007/978-3-031-12641-3_11

Hanifah, Z., & Bastian, I. (2020). Hospital debts and receivables management based on hospital financial reports of debt and receivable in jkn era: A case study RS ABC. E-Prosiding Akuntansi, 2(2). https://doi.org/10.31326/.v2i2.789

Komarraju, A. K., Batchu, V. V., & Mandala, V. (2024). The role of artificial intelligence and machine learning in financial processes: Innovations and impacts on accounts payable, accounts receivable, and general ledger. Nanotechnology Perceptions, 20(8). https://doi.org/10.62441/nano-ntp.v20is8.72

Kramoliš, J., & Dobeš, K. (2020). Debt as a financial risk factor in SMEs in the Czech Republic. Quarterly Journal of Economics and Economic Policy, 15(1), 87–105. https://www.ceeol.com/search/article-detail?id=898791

Laghari, F., & Ahmed, F. (2023). Cash flow management and its effect on firm performance: Empirical evidence on non-financial firms of China. Public Library of Science One, 18(6). https://doi.org/10.1371/journal.pone.0287135

Matolo, D. M. (2023). Effect of the rate of adoption of electronic data interchange on healthcare providers’ cashflow management in Kericho County. Strathmore.edu. https://su-plus.strathmore.edu/server/api/core/bitstreams/a12f0372-d12f-4c1a-9653-7322808b4245/content

Nasution, F., Puspitasari, W., & Saputra, M. (2020). Automation financial processing in account receivable for integrated hospital system using ERP and quickstart approach. Proceedings of the 2020 the 6th International Conference on Frontiers of Educational Technologies, 204–211. https://doi.org/10.1145/3404709.3404759

Pennington, R., & Coustasse, A. (2024). Revolutionizing revenue cycle management: AI’s impact on healthcare organizations. Management Faculty Research. https://mds.marshall.edu/mgmt_faculty/262/

Pounds, L. J. (2021). A framework for artificial intelligence applications in the healthcare revenue management cycle. Proquest.com. https://www.proquest.com/openview/ad22885be9edd4db3a1ae4c89b0c304e/1?pq-origsite=gscholar&cbl=18750&diss=y

Rebuin, E., Murti, W., & Ratnasih. C. (2023). The effectiveness of the measurement of trade receivables through the ratio of the turnover of receivables and the average age ratio of receivables. European Union Digital Library. https://doi.org/10.4108/eai.6-5-2023.2333563

Wassie, F. A. (2021). Working capital management and its impact on firms’ performance: an empirical analysis on Ethiopian exporters. Education Research International, 1–10. https://doi.org/10.1155/2021/6681572

Yeboah, S. (2024). Leveraging receivables: Effect of accounts receivable management on leverage in the healthcare sector. Social Science Research Network. https://doi.org/10.2139/ssrn.4965017