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13 hours ago User’s profile picture OSAMA HAKAMI

Machine Learning for Fetal and Chronic Condition Monitoring: Adapting Global Models to the Saudi Context

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Machine learning (ML) is playing a transformative role in modern healthcare, especially in areas such as telemedicine and remote patient monitoring (RPM). In their work, Ashu and Sharma (2021) presented a novel approach to utilizing machine learning (ML) technologies in conjunction with Internet of Things (IoT)-based wearable medical devices to monitor and manage fetal conditions. Their model demonstrated that real-time health data—such as fetal heartbeats and uterine contractions—can be collected and classified using machine learning (ML) algorithms into categories like normal, suspect, and pathological, which enables early diagnosis and intervention. This model achieved up to 96.23% accuracy after applying linear regression and cross-validation techniques, illustrating the potential for ML to significantly enhance patient care through remote monitoring.

Applying similar ML-based RPM solutions in the Kingdom of Saudi Arabia could provide substantial benefits, especially in regions where access to specialized care is limited. With the widespread availability of smartphones and the country’s investment in digital health infrastructure, such systems could be adapted to monitor chronic conditions like diabetes, hypertension, and maternal health. This would enable healthcare professionals to make data-driven decisions, provide timely interventions, and reduce hospital visits, thereby supporting the nation’s Vision 2030 goals for healthcare accessibility and efficiency.

However, several challenges may arise for biomedical researchers implementing these technologies. One major concern is the handling of unstructured and voluminous health data generated by IoT devices, as noted by Ashu and Sharma (2021). Data preprocessing, storage, and management require robust systems and technical expertise. Additionally, privacy and data security are critical issues, especially when dealing with sensitive patient information transmitted through digital platforms. Researchers must also ensure the reliability and validity of ML models in diverse real-world settings, which require continuous algorithm tuning and validation. Finally, integrating such technologies into existing healthcare systems may face resistance from stakeholders due to a lack of training, infrastructural limitations, or ethical concerns related to automation and patient consent.

In conclusion, machine learning has the potential to revolutionize remote patient monitoring in Saudi Arabia by enabling real-time, accurate health assessments. While the model presented by Ashu and Sharma (2021) provides a successful example within fetal healthcare, broader implementation across the Kingdom will require addressing technological, ethical, and infrastructural challenges to ensure effective and equitable healthcare delivery.

Reference

Ashu, A., & Sharma, S. (2021). A novel approach of telemedicine for managing fetal condition based on machine learning technology from IoT-based wearable medical devices. In Machine learning and the Internet of Medical Things in healthcare (pp. 113–134). Academic Press.

3 hours ago User’s profile picture FAISAL ALFEIFI

Smart Surveillance: Harnessing Machine Learning for Remote Patient Monitoring in Saudi Arabia’s Digital Health Era

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Introduction

Machine learning (ML), a subset of artificial intelligence (AI), enables systems to learn from data without being explicitly programmed. In healthcare, it plays a transformative role by enabling automated, predictive, and personalized care—especially in applications such as Remote Patient Monitoring (RPM). In the context of Saudi Arabia, ML-driven RPM systems hold immense promise for achieving the objectives of Saudi Vision 2030 by enhancing access to care, optimizing health outcomes, and reducing healthcare costs.

Applications of Machine Learning in RPM

ML algorithms can analyze continuous streams of physiological data—such as heart rate, respiratory patterns, oxygen saturation, and body temperature—collected via wearable sensors. These algorithms can detect anomalies, predict deterioration, and generate alerts for clinicians, facilitating early interventions. Ashu and Sharma (2021) explain that ML-powered monitoring platforms can reduce hospital readmissions, support early detection of complications, and improve patient engagement through personalized feedback mechanisms.

Saudi Arabia’s robust digital health infrastructure, strengthened by Vision 2030 initiatives, provides fertile ground for such innovations. Al-Kahtani et al. (2022) report that the digital health transformation in Eastern Province healthcare facilities, particularly in private hospitals, is well underway, with high readiness levels in governance and workforce, and growing capacities in predictive analytics.

Moreover, Alanazi et al. (2024) emphasize that digital technologies like ML and mobile health are integral to expanding access and efficiency in diagnostic and screening services. These capabilities can be extended to home-based monitoring, especially for patients with chronic conditions or those in remote regions.

Challenges Faced by Biomedical Researchers

Despite the potential, several challenges complicate the application of ML in RPM:

Data Quality and Integration

ML models require clean, structured, and interoperable data. Inconsistent inputs from wearable devices, EMRs, and IoT platforms can hinder algorithm performance (Ashu & Sharma, 2021).

Limited Local Datasets

Training ML models on Western datasets may result in cultural or physiological mismatches. There is a critical need to develop Saudi-specific datasets that reflect local patient characteristics and care settings (Al-Kahtani et al., 2022).

Digital Skills Gap

Alanazi et al. (2024) identify a lack of digital literacy and resistance to new technologies among healthcare professionals, including technologists, which limits effective adoption of AI-enabled systems.

Privacy and Ethical Concerns

Sensitive health data must be protected under stringent ethical and legal standards. Alanazi et al. (2024) note that Saudi institutions must address privacy and cybersecurity gaps to ensure trust and compliance.

Infrastructure Constraints

Rural areas and smaller facilities may lack the advanced IT infrastructure required for real-time ML deployment and cloud-based data processing (Ashu & Sharma, 2021).

Conclusion

The integration of ML into RPM has the potential to revolutionize healthcare delivery in Saudi Arabia. As the country accelerates its digital transformation under Vision 2030, addressing challenges related to data infrastructure, ethical governance, local capacity-building, and digital literacy will be key. Policymakers must support this transformation by investing in national datasets, digital health education, and regulatory frameworks that promote safe, effective, and inclusive innovation.

References

Alanazi, M. A., Al-Zughaibi, S. M. N., Al-Harbi, F. S., & Al Dhaheri, M. A. (2024). Digital health integration in radiological screening practices: Opportunities and challenges for technicians in Saudi Vision 2030. Journal of International Crisis and Risk Communication Research, 7(S9), 2499–2507.

Al-Kahtani, N., Alrawiai, S., Al-Zahrani, B. M., Abumadini, R. A., Aljaffary, A., Hariri, B., Alissa, K., Alakrawi, Z., & Alumran, A. (2022). Digital health transformation in Saudi Arabia: A cross-sectional analysis using Healthcare Information and Management Systems Society’s digital health indicators. Digital Health, 8, 1–9.

Ashu, A., & Sharma, S. (2021). Chapter 6—A novel approach of telemedicine for managing fetal condition based on machine learning technology from IoT-based wearable medical device. In K. K. Singh, M. Elhoseny, A. Singh, & A. A. Elngar (Eds.), Machine Learning and the Internet of Medical Things in Healthcare (pp. 113–134). Academic Press.

1 day ago User’s profile picture MUHANNAD ZARBAH

Revenue Cycle Management Metrics in Healthcare Systems

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Revenue Cycle Management (RCM) is a critical financial process in healthcare organizations that encompasses the administration of financial transactions, from patient registration and appointment scheduling to the final payment of a balance. The effectiveness of RCM directly impacts an organization’s financial health and sustainability (Rauscher & Wheeler, 2008). Four key metrics are commonly used to evaluate RCM performance: Days of Service Outstanding (DSO), Net Percentage Collection, Accounts Receivable Over 90 Days, and Bad Debt Percentage.

Days of Service Outstanding (DSO):

DSO measures the average number of days it takes for a healthcare organization to collect payment after services are rendered. This metric is calculated by dividing the total accounts receivable by the average daily revenue and indicates how efficiently an organization converts services into cash (Mindel & Mathiassen, 2015). A lower DSO value suggests more effective collection processes and better cash flow management. According to Rauscher and Wheeler (2008), “DSO is a powerful indicator of a healthcare organization’s operational efficiency in claims processing and payment collection” (p. 49).

Net Percentage Collection:

Net Percentage Collection represents the ratio of actual payments received to the payments expected based on contractual obligations. This metric reflects the effectiveness of a healthcare organization’s billing and collection processes by comparing what was actually collected against what should have been collected (Shi, 2020). A high net collection percentage indicates efficient revenue capture and minimal revenue leakage. Ideally, healthcare organizations should aim for a net collection rate of 95% or higher (Mindel & Mathiassen, 2015).

Accounts Receivable Over 90 Days:

This metric measures the percentage of accounts receivable that remain unpaid after 90 days. A high percentage of AR over 90 days indicates potential issues in the collection process and increased risk of uncollectible accounts (Rauscher & Wheeler, 2008). Best practice standards suggest that AR over 90 days should be less than 25% of total accounts receivable to maintain healthy cash flow (Shi, 2020).

Bad Debt Percentage:

Bad Debt Percentage represents the portion of services that were expected to be reimbursed but ultimately were not collected and had to be written off as losses. This metric is calculated by dividing the total bad debt write-offs by the total charges for a given period (Mindel & Mathiassen, 2015). High bad debt percentages may indicate problems with insurance verification, patient financial counseling, or collection practices.

Most Important Metric:

While all four metrics are essential for comprehensive RCM evaluation, Days of Service Outstanding (DSO) could be considered the most critical indicator of overall RCM health. According to a study by Mindel and Mathiassen (2015), “DSO serves as a holistic measure that reflects the cumulative effectiveness of the entire revenue cycle, from service documentation to final payment receipt” (p. 316). DSO directly impacts an organization’s cash flow and financial liquidity, which are fundamental to operational sustainability.

Furthermore, DSO often reflects problems that might be occurring throughout the revenue cycle. An elevated DSO could indicate issues with claims submission, coding accuracy, payer processing, denial management, or patient collections (Rauscher & Wheeler, 2008). By monitoring and addressing factors that contribute to higher DSO, healthcare organizations can improve other metrics as a result.

Shi (2020) supports this view, noting that “organizations that successfully reduce their DSO typically see corresponding improvements in net collection rates and reductions in accounts receivable aging and bad debt write-offs” (p. 78). This suggests that DSO can serve as both a diagnostic tool and a leading indicator of overall RCM performance.

Conclusion

While an effective RCM strategy requires monitoring all four key metrics, DSO provides the most comprehensive insight into the efficiency of the entire revenue cycle process. By focusing on reducing DSO, healthcare organizations can improve cash flow, identify operational inefficiencies, and ultimately enhance financial performance across all RCM metrics.

References

Mindel, V., & Mathiassen, L. (2015). Configurational effects in health information technology: A study of technology performance in a U.S. hospital system. Journal of the Association for Information Systems, 16(5), 312–345.

Rauscher, C., & Wheeler, J. (2008). Healthcare financial management: Revenue cycle strategies. Healthcare Financial Management Association.

Shi, L. (2020). Introduction to health policy (2nd ed.). Health Administration Press.

1 day ago User’s profile picture RAGHAD BAHRAN

Revenue Cycle Management (RCM) in Healthcare systems .

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Revenue Cycle Management (RCM) in Healthcare

Revenue Cycle Management (RCM) is an essential function in healthcare organizations, focusing on managing the financial transactions that occur throughout a patient’s journey. Effective RCM ensures that hospitals can efficiently collect payments, reduce underpayments, and minimize bad debt, thereby enhancing their financial health and operational sustainability (Chandawarkar et al., 2024) .

Dates of Service Outstanding (DSO)

Definition: DSO is a critical metric that measures the average number of days it takes for a healthcare provider to collect payments for services rendered after the date of service. It is calculated by dividing the total accounts receivable by the average daily revenue.

Importance: DSO provides insight into the efficiency of a hospital’s revenue cycle and its ability to collect payments promptly. A lower DSO indicates faster collection of revenue, improving cash flow. This metric is particularly important for identifying inefficiencies in the collection process and highlights potential delays in reimbursement. A high DSO suggests a need for process improvements or better payer follow-up.

Relevance: This metric is highly critical because it directly affects a healthcare provider’s cash flow, which is crucial for maintaining operations and reinvestment in patient care.

Net Percentage Collection

Definition: The Net Percentage Collection measures the percentage of total revenue a hospital successfully collects after accounting for payer adjustments, discounts, and write-offs. It is calculated by comparing the actual collections to the expected collections based on contractual agreements with insurance providers.

Importance: This metric offers an accurate reflection of how effectively a hospital is converting its gross revenue into actual collected amounts. It is vital for assessing the efficiency of the revenue cycle and ensuring that the hospital maximizes collections within the framework of insurance agreements and patient payments.

Relevance: This metric is essential in understanding the overall financial health of a hospital as it directly correlates to the facility’s ability to generate sustainable revenue.

Accounts Receivable Over 90 Days

Definition: This metric tracks the percentage of a hospital’s accounts receivable that has been outstanding for more than 90 days. It reflects the effectiveness of the institution’s collection processes and how well it handles overdue accounts.

Importance: A high percentage of accounts receivable over 90 days can indicate inefficiencies in the collections process, increasing the likelihood of bad debt. It is a critical metric for identifying areas in need of improvement, as delayed payments can significantly disrupt a hospital’s financial stability.

Relevance: This metric is particularly important for assessing the effectiveness of collection efforts. The longer the receivables remain unpaid, the harder it is to recover the amounts due, leading to potential losses.

Bad Debt Percentage

Definition: The Bad Debt Percentage represents the portion of a healthcare provider’s accounts receivable that is expected to be uncollectible. It is calculated by comparing the uncollectible amounts to the total revenue.

Importance: This metric is crucial for understanding the financial strain on a hospital’s revenue cycle. A high bad debt percentage suggests inefficiencies in billing, collections, or payer agreements, which could lead to significant financial losses. Effective management of bad debt is essential for ensuring that a healthcare provider maintains profitability while minimizing write-offs.

Relevance: This is one of the most important metrics, as it directly influences the hospital’s profitability and operational efficiency. A low bad debt percentage indicates that a hospital is efficiently collecting payments, whereas a high percentage indicates that the organization is struggling with collections.

Conclusion

Each of these metrics plays a vital role in assessing the overall effectiveness of a hospital’s Revenue Cycle Management. Among them, Dates of Service Outstanding (DSO) From (HQS515_Chapter15-Slide 26), explains how Average Collection Period (ACP)—which is essentially the same as DSO—impacts a healthcare provider’s financial performance by directly affects cash flow, operating costs, and overall financial health, making it one of the most critical metrics to monitor. In the context of healthcare in the Kingdom of Saudi Arabia, these metrics are becoming increasingly crucial as the country’s healthcare system continues to evolve with initiatives aimed at improving efficiency and sustainability (Al Yafi et al., 2024). As the Kingdom invests in healthcare reforms under Vision 2030, focusing on revenue cycle management will be key to ensuring that the sector remains financially robust and capable of supporting the growing demands of its population.

References :

Al Yafi, O., Albabtain, M. A., Arafat, A., & Bin Jassas, A. (2024). Adopting health revenue cycle management best practices among public or private healthcare providers in Saudi Arabia: A pilot study. Discover Health Systems, 3(1), 88.

Chandawarkar, R., Nadkarni, P., Barmash, E., Thomas, S., Capek, A., Casey, K., & Carradero, F. (2024). Revenue Cycle Management: The Art and the Science. Plastic and Reconstructive Surgery Global Open, 12(7), e5756.