Artificial Intelligence-Based Hospital Recommendation System

Artificial Intelligence-Based Hospital Recommendation System

*Ekwealor, Oluchukwu Uzoamaka¹, Chukwudum, Chiemeka Prince², Betrand, Chidi Ukamaka³, Uchefuna Charles Ikenna⁴, Ibeh, Charles Austeen⁵

¹ Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria

^2,5Department of Forensic Science, Nnamdi Azikiwe University, Awka, Nigeria

³Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, Owerri, Nigeria

⁴ Department of Computer Science, Federal Polytechnic, Oko, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2025.10040097

Received: 13 April 2025; Accepted: 17 April 2025; Published: 23 May 2025

ABSTRACT

This paper is focused on the development of AI-based hospital recommendation system to enable users find suitable healthcare facilities based on location, ratings, and patient reviews. By leveraging machine learning, the system can analyze the patient data and hospital attributes to provide personalized hospital recommendations. During the system development, hospital data, including location, reviews, and ratings were collected, cleaned and prepared for analysis. Machine learning algorithms, such as collaborative filtering and content-based filtering were employed. The best-performing model was selected based on accuracy metrics to ensure personalized recommendation. The system was implemented using PHP, MySQL, HTML, CSS and JavaScript. It is very helpful in the area of patient decision-making as it provides tailored hospital recommendations.

Keywords: Artificial intelligence, recommendation system, Machine Learning, Deep learning, Content-based filtering, collaborative filtering.

INTRODUCTION

Artificial Intelligence application in health sector has transformed patient care, offering more tailored and efficient services to patients. Artificial Intelligence’s ability of analyzing huge amounts of data, learning from patterns, and making predictions has contributed immensely in the development of hospital recommendation systems, that enables the direction of patients to healthcare facilities that is best suited for their needs. This transformation is in sync with the current technological innovations, that has proved that Artificial Intelligence systems can save decision-making time, increase accuracy, and promote patient satisfaction (Lee & Yoon, 2020).

Recommender systems were initially popular in e-commerce and streaming sectors, where they offered customized suggestions to users. However, over the years, their use extended to other domains including health sector, following their efficiency in management of information overload. In health sector, recommendation systems assists in giving patients directions to the best suited healthcare facilities or specialists based on their preferences, conditions and needs. This promotes patient care as it ensures that they receive appropriate recommendations (Jiang et al., 2021).

Decisions on health issues, especially as it relates to hospitals selection are most times difficult for patients. A lot of people find it challenging getting hospital facilities that correspond with their location, health needs or budget. Artificial Intelligence based hospital recommendation system provides solution these challenges by leveraging data such as locations, ratings and reviews, thereby assisting patients in making informed decisions through suggestion of hospitals based on parameters such as proximity, quality of service, specialization and reviews. Also in cases of constrained  healthcare resources, this recommendation system can assist in better access and allocation (Wang & Gao, 2023).

Review of related work

Many researches have been on-going on the application of Artificial intelligence recommendation systems in health sector, especially in the context of hospital selection. One fundamental approach assessed is the collaborative filtering, a common technique in Artificial Intelligence recommendation systems that leverages patient data to personalize recommendations. Su et al. (2019) pointed out the potency of collaborative filtering in the analysis of patient’s historical information, including health conditions and previous treatments, to provide good quality, personalized hospital recommendations. The value of collaborative filtering is based on its ability to make relevant suggestions using patterns from similar patients, thus making sure that patients with similar cases are directed to hospitals well equipped for handling their specified heath challenges. This approach minimizes the stress of decision making and helps patients by offering them data-driven and informed options.

Content-based filtering is another commonly used method that offers recommendations based on the kind of medical services or hospitals instead of the patient similarities. Kim et al. (2020) stated the important of content-based systems in matching patients with hospitals according to the attribute of the hospital, such as specialization, medical expertise and equipment. This approach is very helpful in  recommending specialist hospitals that specialize in rare illnesses or uncommon conditions, as it assists patients in selecting hospitals that have what it takes to tackle their health challenges .Content-based filtering offers an effective means of finding appropriate hospital for patients that require specialized treatment without undergoing     excessive independent research.

A more advanced approach combines collaborative and content-based filtering into hybrid recommender systems, which have shown considerable promise in healthcare. Zhang et al. (2018) demonstrate the enhanced accuracy and effectiveness of hybrid models in healthcare settings, where hospital recommendations benefit from integrating patient preferences with detailed hospital profiles. By leveraging both user data and facility characteristics, hybrid systems offer precise hospital suggestions that go beyond single-method approaches. This synthesis of data allows for a broader, more personalized view of hospital options and optimizes the alignment between patient needs and hospital capacities.

The document also delves into AI-powered chatbots, which serve as interactive recommendation tools. Chen et al. (2021) illustrate how these chatbots engage patients by asking targeted questions about their symptoms, preferences, and healthcare goals. Chatbots make healthcare more accessible by providing real-time hospital recommendations tailored to users’ immediate needs. This is especially beneficial for individuals in rural or underserved areas who may otherwise face barriers to accessing specialized healthcare advice. Chatbots thus streamline the recommendation process, enabling patients to receive timely and relevant hospital guidance.

Context-aware recommender systems add an additional layer of customization by considering factors like geographic location, time of day, and the urgency of medical needs. Mahmood et al. (2017) emphasize that context-aware systems make recommendations that are not only personalized but also timely, as they take situational variables into account. This proves invaluable in emergency situations where patients require immediate care. By prioritizing proximity and real-time availability, context-aware recommender systems ensure patients receive appropriate care without unnecessary delays, thereby improving patient outcomes.

Another key area of focus is the integration of electronic health records (EHR) into hospital recommender systems. Yin et al. (2019) demonstrate how utilizing EHR data, including patient history and prior treatments, enhances recommendation accuracy. EHR-based systems can facilitate continuity of care by providing insights into patients’ health trajectories and aligning recommendations with their established healthcare needs. This integration not only supports a more seamless patient experience but also assists healthcare providers in delivering informed and personalized care.

The document also highlights the critical issue of data privacy and security in hospital recommender systems. Smith et al. (2020) showed the benefit of encryption and anonymization techniques in handling sensitive health information. With a very high regulation of healthcare data, we can have secure systems that maintain patient trust. Data protection techniques enable AI-driven recommendations to safely leverage patient data while sticking to legal and ethical standards, making these systems very effective and safe for patient use.

Machine learning (ML) and predictive analytics also play important role in developing hospital recommender systems. Gupta et al. (2021) detailed how predictive algorithms are able to forecast patient outcomes, like the rates of treatment success, direct patients to hospitals that has higher success rates in treating specific medical cases. By analyzing historical data, these systems are able to suggest hospitals with demonstrated expertise in specific treatments, offering patients recommendations on the basis of empirical evidence instead of subjective measures. This predictive ability promotes patient confidence in making healthcare choices and promotes informed decision-making.

Deep learning, a specialized area of machine learning with great potential in refining hospital recommender systems was also explored. Liu et al. (2022) demonstrated how deep learning techniques can be used to process huge amounts of patient data, such as demographic details, health conditions and personal treatment preferences. Through modeling complex relationships within this data, deep learning offers more personalized and precise recommendations. Deep learning’s capability of analyzing diverse types of patient data facilitates in making highly individualized suggestions, accommodating a wide range of healthcare needs.

In emergency situations, hospital recommender systems often prioritizes speed and accuracy. Zhao et al. (2020) examined AI-based systems for recommending the nearest hospitals with available emergency services with the aim to reduce patient wait times. This helps the individuals to receive prompt care in critical conditions, hence improves the likelihood of positive health outcomes. Through emphasis on proximity and immediate availability, these systems offer a practical solution for time-sensitive medical needs.

Another emerging and effective tool in hospital recommendation systems is Natural language processing (NLP). it assists in the interpretation of unstructured patient data, such as doctors’ notes or patient reviews. Chou et al. (2021) showed how NLP can improve the relevance of hospital suggestions through analysis of patient experiences and feedback. This incorporation of qualitative data enhances the overall quality and reliability of recommendations, as the system can integrate personal patient insights often overlooked in conventional data analysis.

The concept of patient-centric recommendation systems was also explored, with focus on promoting patient satisfaction and overall experience. Adebayo et al. (2019) emphasized the importance of incorporating patient satisfaction metrics into hospital attributes, such as hospital amenities and quality of treatment in order to provide recommendations in line with patient expectations. Through prioritizing patient-centered care, these systems promote trust and ensure that hospital suggestions resonate with patients’ preferences and needs, thereby, promoting a better healthcare experience.

Ibrahim et al. (2021) in their work, demonstrated how fuzzy logic interprets ambiguous or incomplete information such as patient symptoms or hospital capacity. This ability makes fuzzy logic an effective solution in cases where data is not very clear, thus, offering patients reliable recommendations even in the face of uncertainty. Through accounting for variability od data, fuzzy logic has contributed to a more robust and flexible recommendation task.

Wu et al. (2020) highlighted ability of blockchain technology to protect patient data integrity and ensuring that recommendation processes are secure and verifiable. Blockchain’s tamper-proof nature provides solution for data privacy concerns, making it a promising addition to AI-based systems requiring high degree of trust and transparency, especially while working on sensitive healthcare information.

Finally, Verma et al. (2019) illustrated how Artificial Intelligence can analyze hospital expertise in handling chronic diseases like diabetes or cardiovascular conditions, in order to provide more relevant hospital suggestions. Through identification of facilities with specialized knowledge and experience, these systems assist patients in accessing appropriate care customized to their long-term health needs.

Artificial Intelligence in Healthcare

Currently, Artificial Intelligence is rapidly transforming healthcare sectors through support for critical functions such as diagnostics, treatment recommendations, patient monitoring, and hospital management. Recent innovations emphasize ability of Artificial Intelligence to automate repetitive tasks, streamline workflows and improve accuracy in medical diagnoses, which significantly improve medical outcomes (Silva et al., 2021; Johnson et al., 2020).

Machine learning, an important branch of AI, has been found useful in helping systems learn from historical data in order to make predictive recommendations and assisting healthcare providers in handling complex conditions efficiently. This ability has significantly proven valuable in the analysis of large datasets, provision of insights that helps physicians make informed decisions, thereby, optimizing the overall care (Gupta et al., 2021).

AI is prominently applied in the area of predictive analytics, where historical and real-time data are used in predicting patient outcomes such as the risk of disease progression or the likelihood of hospital readmission. Through early identification of high-risk patients, hospitals can intervene timely enough to reduce complications and healthcare costs (Han et al., 2021). AI models also facilitate clinical decision support through the generation of data-driven insights that promote patient safety and work efficiency. This predictive ability is particularly useful in intensive care units, where AI systems are able to track patient vitals and alert staff for potential emergencies (Chen et al., 2021).

Today, AI-driven solutions like IBM Watson and DeepMind are being deployed in clinical settings for diagnosing and recommending treatments, hence fostering tailored and precise medical care (Kambatla & Chung, 2021). These systems integrate data from different sources, providing recommendations that align with a patient’s unique health profile. Artificial Intelligence’s diverse roles in personalized medicine shows its potential in enhancing patient engagement and satisfaction by providing tailored care options that enhance health outcomes. As AI tools continue to evolve, their applications in healthcare sector continues to expand, hence. making customized, efficient, and proactive care easily accessible to diverse patient population.

Recommendation Systems

Recommendation systems are a subset of Artificial Intelligence developed to offer tailored suggestions to users based on their past behavior, preferences or needs. Having their origin from e-commerce and content platforms such as Amazon and Netflix, recommendation systems have penetrated into diverse industries, including healthcare sectors (Ricci et al., 2011). In healthcare sector, recommendation systems are used in suggesting relevant treatments, therapies or healthcare facilities to patients.

Koren and Bell (2015) opined that recommendation systems utilize two main techniques, collaborative filtering and content-based filtering. While Collaborative filtering leverages the preferences and experiences of other users or patients to make recommendations, Content-based filtering focuses on the specific characteristics of items such as hospitals or treatments to recommend options to users.

In hospital recommendation systems, these two techniques are employed in the analysis of patient preferences, treatment outcomes, hospital specialties, and geographic factors so as to suggest the best suited hospitals for patients. According to Nguyen et al. (2020), healthcare recommendation systems play an important role in encouraging patient autonomy by helping them make informed decisions concerning their healthcare options. These systems minimize the problem of choice for patients, particularly in situation of multiple hospital options with different levels of expertise and treatment success rates.

Machine Learning Techniques in Recommendation Systems

Machine learning (ML) is an important part of AI and it is very essential for developing sophisticated recommendation systems. Some of the machine learning techniques used in recommendation systems include decision trees, neural networks, support vector machines, and clustering algorithms (Aggarwal, 2016). These techniques enable the system to learn from historical data, make predictions, and continuously improve the accuracy of its recommendations over time. For instance, collaborative filtering uses matrix factorization techniques such as singular value decomposition (SVD) to identify patterns in the behavior of users and make predictions based on that data.

In developing hospital recommendation systems, machine learning can be used in analyzing large datasets of patient preferences, reviews and hospital performance metrics. Zhang et al. (2019) in their study, opined that machine learning techniques could be used to predict the best hospital for a patient based on given factors such as rate of treatment success, hospital facilities and geographic proximity. Also, deep learning, a subset of machine learning, was explored in healthcare recommendation systems for its capability in modeling complex relationships between patient needs and hospital services (Liu et al., 2020). The adoption of machine learning in hospital recommendation systems tends to promote the quality and relevance of the recommendations provided to patients.

Personalization in Hospital Recommendation System

Personalized recommendation is a crucial aspect of healthcare recommendation system as patients have unique medical needs, preferences and constraints. A hospital recommendation system must take into consideration these individual factors to deliver personalized recommendations that are relevant and actionable for the patient (Ricci et al., 2021). Personalized healthcare is becoming increasingly important as patients demand more autonomy and tailored treatment options. In healthcare, personalization includes recommending particular hospitals based on treatment specialties, offering suggestions based on the insurance plan of the patient or geographic location.

Using patient-centric data like medical history, demographics and preferences, help recommendation systems in delivering more accurate and relevant suggestions. According to Johnson et al. (2020), recommendation systems that prioritize personalization tend to promote patient satisfaction and trust in the healthcare system.

More so, personalization minimizes the risk of patients receiving irrelevant or trivial recommendations, that can result to dissatisfaction or poor health outcomes. incorporating AI and machine learning into tailored recommendations is very crucial to ensure that patients receive the best possible care customized to their specific needs.

Operation of the System

The system is a significant innovation in the healthcare sector. It providepatients with guide on healthcare facilities best suited to their medical needs. the system leverages Artificial Intelligence algorithms and machine learning models to analyze a combination of factors such as patient medical history, location, symptoms, and preferences, along with hospital-specific data like specialization, availability of doctors, and past patient reviews. By synthesizing this information, the system would generate personalized recommendations, helping patients find hospitals that match their specific healthcare requirements. This could be particularly beneficial in regions with multiple hospitals and healthcare providers, where patients often face the challenge of selecting an appropriate facility without a clear understanding of its capabilities or specialization.

The system’s architecture was be built upon various machine learning techniques, such as collaborative filtering and content-based filtering, to process patient and hospital data. Collaborative filtering would allow the system to recommend hospitals based on similarities with previous patients who had comparable needs and experiences. Additionally, content-based filtering could help identify facilities based on the specific services they offer, taking into account the patient’s medical requirements. Natural language processing (NLP) could enhance the system’s ability to interpret and categorize user reviews and other unstructured data, providing valuable insights into each hospital’s strengths and weaknesses. This approach would ensure that recommendations are accurate and genuinely aligned with individual patient needs.

In terms of data handling and security, the system would need to prioritize data privacy and regulatory compliance, especially given the sensitive nature of healthcare data. Patient data security would be maintained through encryption and secure data storage mechanisms ensuring that personal health information (PHI) is protected against unauthorized access.

CONCLUSION

ArtificialIntelligence recommendation system for hospitals is a significant effort towards advancing health care sectors by enhancing patient care and optimizing healthcare delivery. Through leveraging machine learning algorithms and comprehensively analyzing patient needs and hospital capabilities, the system offers a personalized recommendations that help patients in making informed decisions about their health condition. This research work also addresses various issues encountered  the navigation of complex healthcare landscape, thereby optimally improving access to suitable medical facilities.

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