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Data-Driven Machine Learning Approaches to Cut Hospital
Readmissions in USA
1Tahmidur Rahman Chowdhury,2 Mizanur Rahman, 3Faysal Ahmed, 4Shamima Afrose, 5Md Adnan
Sami Bhuiyan
1Ambassador Crawford College of Business and Entrepreneurship, Kent State University, Kent, Ohio,
USA.( First and Contracting Author)
2Institute of Health Economics, University of Dhaka, Bangladesh
3Wright State University, Dayton, Ohio, USA.
4Govt. College of Applied Human Science, University of Dhaka, Bangladesh
5DePauw University, Greencastle, IN, USA.
DOI: https://doi.org/10.51584/IJRIAS.2025.100900089
Received: 21 Sep 2025; Accepted: 28 Sep 2025; Published: 23 October 2025
ABSTRACT
Readmissions in hospitals are a major challenge to healthcare systems globally and lead to increased cost,
burden on clinical resources, and poor patient outcomes. Conventional methods of identifying readmission risk
that commonly rely on rule-based models and clinician judgment have demonstrated poor predictive validity.
New developments in machine learning (ML) offer potent alternatives, via the utilization of vast amounts of
structured and unstructured healthcare information to detect intricate patterns, related to the risk of
readmission. This paper will discuss the use of machine learning models, including logistic regression, random
forests, gradient boosting and deep learning, to predict hospital readmissions in a variety of patients. We speak
about the contribution of electronic health records (EHRs), demographic factors, comorbidities, medication
adherence, and post-discharge follow-up variables to the enhanced model performance. Explainable AI
methods are given a special focus to make the model prediction transparent and trusted by clinicians. Other
important challenges that are identified in the review are data quality, class imbalance, bias, and
generalizability in healthcare settings. Case studies reveal how predictive models can be used to initiate
specific interventions, including improved discharge planning, telehealth monitoring, and tailored care
coordination, which can in turn lead to a reduction in avoidable readmissions and eventually lead to better
patient outcomes. A combination of machine learning with clinical workflows will enable healthcare
organizations to transition to more proactive, data-driven, and cost-beneficial care. The results highlight the
disruptive power of machine learning to solve the long-standing problem of hospital readmissions and define
the areas of future research in designing ethical applications, interoperability, and policy.
Keywords: Patient Experience, Patient Satisfaction, Healthcare Quality, Communication Strategies,
Technology Integration, Empathy Training, Environmental Improvements, Operational Efficiency, Patient-
Centred Care.
INTRODUCTION
The issue of hospital readmission has been a burning topic in contemporary healthcare both as a measure of the
quality of patient care provided in the initial hospitalization and as a measure of the quality of post-discharge
management. A hospital readmission is generally described as a sudden reappearance of a patient in a hospital
in a given time span usually 30 days after discharge. It is well understood that high readmission rates reflect
ineffective care delivery and poor health outcomes, which is why healthcare systems and policymakers are
keen on developing measures to help reduce them.
Readmission costs are quite high in terms of cost and clinical care. In the United States alone, readmissions
have been estimated to cost billions of dollars every year; imposing significant financial burden on healthcare
systems and insurers. In addition to the economic consequences, repeated readmissions interfere with the
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recovery of patients, predisposing them to complications, and reducing life quality in general. Therefore, this
problem is not only a cost-containment issue, but also an essential element of enhancing patient-centered care.
Conventional readmission risk prediction tools, including clinical judgment tools and rule-based scoring
systems have added some value but are often incomplete in terms of accuracy and scalability. These methods
are typically based on a limited number of clinical variables and do not reflect the multidimensional nature of
demographic, behavioral, and social determinants of health that lead to readmission risk. As a result, a large
number of high-risk patients end up undetected and resources end up being redirected to less risky patients.
Over the last few years, machine learning (ML) has come to the fore as a feasible predictive healthcare system.
Using high volumes of electronic health records (EHRs), claims information, and other data of interest, the ML
algorithms are capable of identifying trends and nonlinear relationships that the traditional statistical models
can fail to reveal. Such data-based models have the potential to offer more precise, timely, and personalized
readmission risk predictions, which can subsequently support specific interventions, including improved
discharge planning, post-discharge care, and telehealth follow-up.
This study aims to investigate how machine learning models would decrease the rates of hospital readmission.
In particular, it deals with the drawbacks of the traditional risk prediction methods, the advantages of different
ML algorithms, and their implementation in clinical practice. This paper is limited to issues related to
implementation, such as data quality, interpretability, and ethical issues, but it also delineates future directions
to use machine learning to improve patient outcomes and reduce readmission rates by integrating it into
healthcare systems.
Understanding Hospital Readmissions
Definition & Classification
A hospital readmission occurs when a patient admitted to a hospital within a short period after being
discharged from an earlier hospitalization (the "index admission") returns—commonly within a 30-day
window
Readmissions can be:
1. Planned (e.g., scheduled follow-up surgeries or procedures),
2. Unplanned or all-cause readmissions, which are considered indicators of care gaps and are the focus of
most quality-improvement measures
The 30-day timeframe is widely used, especially by policymakers and payers, because readmissions within this
period are more likely linked to initial care quality and discharge processes
Key Causes of Hospital Readmissions
Multiple factors contribute to readmissions, including:
1. Clinical complications: e.g., postoperative infections, unresolved symptoms, medication-related
problems.
2. System-level issues: poor discharge planning, ineffective handoffs, and insufficient follow-up.
3. Quality of care concerns, such as inadequate patient education or care coordination
Role of Patient Demographics, Comorbidities & Socioeconomic Factors
Demographic and health-related factors heavily influence readmission risk:
1. Comorbidities: Patients with multiple chronic or severe conditions (like blood diseases, cancers, or
circulatory system disorders) often have higher readmission rates
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2. Socioeconomic determinants: Insurance types—particularly Medicare and Medicaid—are associated
with elevated readmission rates
3. Racial and geographic disparities: In 2020, readmission rates were highest among non-Hispanic Black
patients (16.0 per 100 admissions) and among those residing in large central metro areas (~14.6 to 14.8
vs. rural areas at ~13.0)
Policy & Regulatory Implications
In the U.S., the Hospital Readmissions Reduction Program (HRRP)—mandated under the ACA—imposes
financial penalties on hospitals with excess unplanned 30-day readmission rates for specific conditions (e.g.,
heart failure, pneumonia, COPD, hip/knee replacements, and CABG surgery)
1. Hospitals' performance is quantified using the Excess Readmission Ratio (ERR), comparing observed
vs. expected readmissions, and penalties can reach up to 3% of Medicare reimbursement
2. The program started applying penalties in 2013, with additional conditions added subsequently
3. Adjustments were introduced to account for hospitals serving disproportionate shares of low-income or
dual-eligible patients, recognizing socioeconomic disparities in patient populations
Readmission rates are also used by organizations (like NCQA) to track unplanned readmissions as a quality
metric, serving as indicators of care coordination efficacy
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Machine Learning in Healthcare
Overview of ML Techniques Relevant to Predictive Healthcare
Machine learning (ML) is transforming healthcare by enabling data-driven predictions that go beyond
traditional rule-based methods. In the context of hospital readmissions, ML techniques are particularly
valuable for handling complex, high-dimensional datasets. Common approaches include:
1. Supervised learning models such as logistic regression, decision trees, random forests, support
vector machines (SVMs), gradient boosting machines (e.g., XGBoost, LightGBM), and deep
neural networks. These models learn from labeled patient data to predict outcomes like readmission
risk.
2. Unsupervised learning techniques such as clustering (k-means, hierarchical clustering) to identify
hidden patient subgroups with similar risk profiles.
3. Reinforcement learning, which is emerging in healthcare for optimizing personalized interventions
and care pathways by learning from sequential decision-making processes.
4. Natural language processing (NLP) applied to unstructured data like clinical notes, discharge
summaries, and patient feedback to extract insights that complement structured data sources.
These methods can model nonlinear relationships, capture subtle interactions among variables, and provide
more personalized risk predictions than conventional techniques.
Comparison of ML with Traditional Statistical Models
Traditional risk prediction tools, such as logistic regression or Cox proportional hazard models, have been
widely used in clinical practice due to their interpretability and ease of implementation. However, these models
often assume linear relationships and independence between predictors, which limits their predictive power
when applied to complex healthcare datasets.
In contrast, ML models can:
1. Handle large and heterogeneous datasets, including both structured (e.g., demographics, lab results,
medications) and unstructured data (e.g., clinical notes).
2. Capture nonlinear interactions and higher-order dependencies among predictors.
3. Continuously learn and adapt as new data becomes available.
While ML offers superior predictive performance in many cases, it also introduces challenges, such as
interpretability and potential bias. Explainable AI (XAI) techniques, like SHAP (SHapley Additive
Explanations) and LIME (Local Interpretable Model-agnostic Explanations), are increasingly important to
ensure clinicians can understand and trust model outputs.
Importance of Big Data and Electronic Health Records (EHRs)
The adoption of electronic health records (EHRs) and advances in health informatics have provided the
foundation for ML applications in predictive healthcare. EHRs capture vast amounts of patient-level data,
including:
1. Demographics (age, sex, socioeconomic status).
2. Clinical information (diagnoses, lab test results, comorbidities, medications, vital signs).
3. Utilization patterns (hospital visits, emergency department usage, post-discharge follow-ups).
4. Social determinants of health (housing stability, insurance type, access to primary care).
When integrated with claims data, wearable devices, and genomic information, EHRs enable the construction
of rich, multi-dimensional datasets that can significantly improve the predictive accuracy of ML models.
The importance of big data lies not only in its size but also in its variety, velocity, and veracity. By
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leveraging advanced algorithms, healthcare organizations can detect at-risk patients earlier, personalize care
strategies, and optimize resource allocation to reduce preventable readmissions.
Machine Learning Models for Readmission Prediction
Logistic Regression and Baseline Models
Logistic regression (LR) is a common baseline used to predict hospital readmissions because it is easy to
understand and interpret. As an example, a paper claimed an out-of-sample accuracy of about 0.706 and AUC
of about 0.661 of LR in 30 days readmission predictions Nonlinear relationships can be harder to predict with
LR, however, compared to more sophisticated models.
Decision Trees and random forest.
Decision trees (DTs) provide directly interpretable predictions in a rule form, although they are vulnerable to
overfitting. RFs using bagging to combine individual decision trees improve stability, accuracy, and reduce
overfitting For example, RF models do better on readmission tasks compared to LR and DT in multiple health
care datasets in a balanced random forest, setting sensitivity at 0.70 and AUC at 0.78 PubMedMDPI.
Gradient Boosting Machines (XGBoost, LightGBM)
Gradient boosting algorithms, such as GBM, XGBoost (XGB), and LightGBM (LGBM) construct learners in
sequence to correct past errors and can frequently achieve better predictive accuracy. In one study, the models
based on GBM performed significantly better than LR and baseline features in predicting readmissions as
indicated by an AUC of around 0.83 versus LR at 0.66 PubMed. In particular, XGBoost can perform well on
pediatric readmission prediction tasks, reaching an AUC of 0.814 PubMed. XGBoost was also better than rule-
based models BioMed Central in pneumonia cases.
Artificial Intelligence and Deep Learning.
Neural networks (NNs) outperform LR in AUC on readmission tasks at each specialty BioMed Central. Even
more powerful predictive capabilities are being driven by advanced deep learning architectures, including
transformer-based models, and spatiotemporal graph neural networks. A transformer-based model which used
EHR, images, and notes (PT model) was shown to be highly accurate and resilient- even when the temporal
data was not available arXiv. A multimodal, spatiotemporal graph neural network-based approach also
obtained an AUROC of 0.79, which is far superior to traditional clinical scores, such as LACE+ (AUROC
0.61) arXiv.
Higher Accuracy Ensemble Learning Techniques.
Other ensemble techniques include stacking, boosting and hybrid models, which are multiple algorithms that
are used in combination to improve performance. Ensemble Stacking RF, GBDT and boosting techniques were
combined in a stacking-based ensemble, which enhanced sensitivities and the overall AUC- suggesting that
ensembling has unambiguous advantages in readmission prediction tasks BioMed CentralPubMed. In other
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research, a mixed ensemble (SVM + C5.0 decision tree) was used on CHF readmissions and reported 81 to 85-
percent accuracy, which implies that ensemble models can perform better than their single counterparts,
particularly when data is imbalanced.
Data Sources and Features
Electronic Health Records (EHRs)
The foundation of healthcare predictive modeling is based on EHRs. These systems have rich, longitudinal
patient information, including demographics, diagnoses, medications, lab results, vital signs,
admissions/discharges, imaging, and clinical notes Wikipedia+1.
The raw, sequential data of EHR can be effectively used by deep learning models, such as the FHIR-based
approach that uses large, unstructured records to predict outcomes, such as 30-day unplanned readmissions at
an AUC of approximately 0.75 0.76 arXiv.
Clinical and Diagnostic Information.
It encompasses systematic clinical variables such as diagnosis codes, comorbidities, history of procedures, lab
test results and findings of imaging. Research points to the benefits of using both manually developed features
and automatically generated longitudinal variables using administrative or hospital data to improve
readmission prediction models.
Demographics and Lifestyle Data.
Age, gender, ethnicity, and the insurance/payment status are demographic variables and are strong predictors.
An example of this is age and chronic illnesses such as kidney disease are critical in readmission risk models
Frontiers. Harder to measure, but lifestyle factors such as smoking, physical activity, and living conditions do
add more sophistication to model accuracy where they are available.
Live Tracking and IoT Health Records.
In the Internet of Medical Things (IoMT) a new layer of real-time patient measurements is presented: wearable
devices (heart rate, activity, glucose), home sensors, smart devices (e.g., smart beds) Wikipedia. Remote
patient monitoring (RPM) has been shown to be useful in chronic conditions - e.g., monitoring glucose trends
in diabetes, or early warning of patient falls in dementia - and potentially be useful in preventing readmissions
as a means of early intervention Wikipedia.
In general, the literature focuses on incorporating IoT data into EHRs to address the issues of scale, speed, and
heterogeneity, which will enhance the ability to predict outcomes and monitor patients
scholarsbank.uoregon.eduSpringerOpen.
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Feature Engineering and Selection for Model Training
successful readmission prediction requires thoughtful feature engineering and selection:
1. Manual feature engineering: Features are defined by domain experts based on time-based or severity-
based features or terms of interaction. As an example, investigators in Alberta (Canada) applied derived
features based on longitudinal hospital data in improving predictive models BioMed Central.
2. Automated feature generation: Models can automatically extract temporal patterns, frequency counts
and aggregates (e.g., number of admissions in past year) and operate at scale with a reduced number of
human interventions BioMed Central.
3. Deep learning-based representation learning: Systems such as Deepr learn to identify predictive
“clinical motifs” directly from sequence-formatted EHR data via convolutional neural networks without
manually engineering features arXiv.
4. Multimodal integration: More sophisticated models integrate EHR structured data with unstructured
data such as clinical notes through graph or transformer-based frameworks. In the case of clinical notes
and structured EHR inputs to graph neural networks, the combination gave an AUROC of about 0.72
arXiv. Likewise, hybrid approaches that use topic modeling (BERTopic) on text and LSTM on
quantitative data achieved AUROC -0.80 in ICU readmission prediction PMC
Model Evaluation and Performance Metrics
Evaluating machine learning models for hospital readmission prediction requires robust and multidimensional
performance metrics. Unlike traditional tasks where simple accuracy may suffice, predicting patient outcomes
has real-world implications for healthcare systems, resource allocation, and patient safety.
Accuracy, Precision, Recall, and F1-Score
1. Accuracy measures the proportion of correctly predicted cases (both readmissions and non-
readmissions) over all cases. While useful, it can be misleading in imbalanced datasets where non-
readmissions far outnumber readmissions.
2. Precision indicates the proportion of true positives (correctly predicted readmissions) among all
predicted positives. High precision ensures fewer false alarms.
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3. Recall (Sensitivity) captures the proportion of actual readmissions that were correctly identified. A
high recall reduces missed high-risk patients.
4. F1-Score is the harmonic mean of precision and recall, balancing the trade-off between avoiding false
positives and false negatives.
Area Under the Curve (AUC-ROC)
The receiver operating characteristic (ROC) curve is a curve that shows the sensitivity (recall) versus
specificity at different thresholds. The AUC (Area Under Curve) is used to summarize the ROC into a single
number whose values near 1.0 represent a good model performance. AUC is very popular in healthcare due to
its ability to measure class imbalance and its threshold-independent evaluation.
Cross-Validation and External Validation
1. Cross-validation (e.g., k-fold CV) ensures that performance metrics are not overly optimistic by
training and testing on different data splits.
2. External validation evaluates models on independent datasets from other hospitals or health systems,
testing generalizability. This step is crucial since patient demographics, care practices, and
socioeconomic contexts vary widely.
Interpretability of Results (SHAP, LIME)
Black-box models like deep neural networks or gradient boosting machines provide strong predictive power
but lack transparency. Interpretability tools bridge this gap:
1. SHAP (SHapley Additive exPlanations): Provides feature-level contributions for each prediction,
highlighting which factors (e.g., comorbidity, age, lab results) drove the decision.
2. LIME (Local Interpretable Model-agnostic Explanations): Creates simple surrogate models to
approximate and explain predictions locally. These approaches enhance trust, accountability, and
adoption among clinicians, who require not just predictions but also rationales for actionable insights.
Applications and Case Studies
Hospital readmission predictive machine learning models are not merely ideas anymore, but they are being
actively implemented into healthcare systems around the world. Their applications describe the way forward
analytics may lead to tangible enhancements in patient outcomes, cost savings, or efficiency.
ML-Based Readmission Reduction in Hospitals
Several hospitals have adopted ML-driven solutions to flag high-risk patients before discharge. For instance:
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1. Mayo Clinic implemented predictive models using EHR and demographic data to identify patients at
elevated risk of readmission, enabling targeted follow-ups.
2. Mount Sinai Health System used random forest algorithms to predict 30-day readmissions for heart
failure patients, which reduced penalties from Medicare by lowering avoidable readmissions.
Predictive Care Planning and Early Interventions
ML models help clinicians tailor discharge plans and allocate resources efficiently. For example:
1. High-risk patients may be scheduled for more frequent follow-ups, given additional discharge
education, or assigned case managers.
2. Risk stratification enables hospitals to prioritize patients most likely to benefit from transitional care
programs.
Integration with Clinical Decision Support Systems (CDSS)
By embedding ML algorithms into CDSS platforms, clinicians can receive real-time risk alerts during patient
care workflows. This integration ensures that predictive insights are actionable and seamlessly incorporated
into existing clinical practices.
Telemedicine and Remote Patient Monitoring Applications
The expansion of telehealth and IoT-enabled monitoring has amplified the power of ML.
1. Wearable devices can track vital signs (e.g., heart rate, blood pressure, oxygen levels) and transmit
data to predictive models.
2. Remote monitoring systems help identify early signs of deterioration, triggering timely interventions
that prevent readmissions.
3. Telemedicine follow-ups supported by ML predictions allow providers to check in with high-risk
patients proactively.
Together, these applications demonstrate that ML-driven readmission prevention is not only a tool for
prediction but also a strategic enabler of proactive, patient-centered care.
Challenges and Limitations
While machine learning (ML) has shown immense potential in reducing hospital readmission rates, several
challenges and limitations need to be addressed before its large-scale adoption in healthcare systems.
Data Quality, Missing Values, and Bias
1. Incomplete or inaccurate data in electronic health records (EHRs) can degrade model performance.
Missing lab results, unstructured physician notes, or inconsistent coding practices introduce errors.
2. Bias in datasets is a major concern: if certain populations (e.g., minorities, rural patients, uninsured
individuals) are underrepresented, models may produce inequitable outcomes.
3. Data harmonization across hospitals remains a critical obstacle, as EHR formats vary widely.
Ethical and Privacy Concerns in Healthcare Data Usage
1. Patient data is highly sensitive, and its use raises HIPAA (Health Insurance Portability and
Accountability Act) and GDPR compliance challenges.
2. ML applications must ensure data anonymization, secure storage, and restricted access to prevent
breaches.
3. There is also the ethical question of informed consent — whether patients are fully aware that their data
is being used for predictive analytics.
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Lack of Interpretability in Complex ML Models
1. Advanced models like deep neural networks often function as “black boxes.”
2. Clinicians may hesitate to trust predictions without explainable AI (XAI) techniques such as SHAP and
LIME that clarify which features drive risk scores.
3. Lack of transparency can hinder adoption, especially in life-critical decisions like patient readmission
risk.
Integration Barriers into Clinical Workflows
1. Even highly accurate ML models may fail if they are not integrated into hospital systems in a user-
friendly and timely manner.
2. Overloaded clinicians may struggle with “alert fatigue” if risk notifications are poorly designed.
3. Aligning ML outputs with clinical decision support systems (CDSS) and hospital IT infrastructure
requires substantial resources and organizational change.
Generalizability Across Populations and Healthcare Systems
1. A model trained in one hospital may not perform well in another due to differences in population
demographics, treatment protocols, and healthcare infrastructure.
2. Overfitting to local datasets can limit scalability.
3. Building federated learning systems and conducting multi-center validation are necessary to improve
generalizability and fairness.
Future Directions
Machine learning (ML) usage in the field of hospital readmission reduction remains in its infancy, and multiple
opportunities are available to increase its efficiency and utilization. The way to move forward is to continue
with technological innovation, remove some of the existing constraints, and keep up with the healthcare
policies and needs of patients.
Federated Learning and Privacy-Preserving ML Approaches
1. Traditional ML requires centralizing patient data, raising privacy and compliance concerns.
2. Federated learning (FL) allows models to be trained across multiple hospitals and institutions without
directly sharing patient data.
3. This enhances generalizability, reduces bias, and ensures compliance with privacy regulations (e.g.,
HIPAA, GDPR).
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4. Future ML frameworks will increasingly rely on differential privacy and homomorphic encryption to
maintain patient confidentiality while enabling large-scale collaborative research.
Integration of IoT and Wearable Data for Real-Time Monitoring
1. Beyond EHRs, healthcare is moving towards continuous patient monitoring through IoT devices and
wearables (e.g., smartwatches, biosensors, home monitoring kits).
2. These devices provide real-time data on vital signs, medication adherence, and lifestyle behaviors—key
predictors of readmission risk.
3. ML models enriched with streaming data pipelines will allow proactive interventions, such as sending
alerts to clinicians when early signs of deterioration are detected.
Explainable AI (XAI) to Improve Clinician Trust
1. One major barrier to ML adoption is the “black box” problem in deep learning.
2. XAI techniques such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-
Agnostic Explanations) can help clinicians understand why a patient is flagged as high-risk.
3. Increased interpretability will build trust, support ethical decision-making, and encourage broader
adoption in hospitals.
4. Future systems may feature human-in-the-loop AI, where clinicians validate or adjust model
recommendations.
Policy Support and Reimbursement Models for ML-Based Interventions
1. Despite technological advances, ML adoption will remain limited unless supported by policy and
economic incentives.
2. Governments and payers (e.g., Medicare in the U.S.) need to establish reimbursement frameworks for
predictive analytics and ML-driven interventions.
3. Policies that promote interoperability standards and data-sharing frameworks will accelerate cross-
institutional collaboration.
4. Hospitals that successfully integrate ML into clinical workflows could benefit from reduced penalties for
avoidable readmissions.
Long-Term Vision: Predictive, Preventive, and Personalized Healthcare
1. The ultimate goal is to shift from reactive care (treating patients after readmission) to proactive care
(preventing readmission altogether).
2. Predictive healthcare uses ML to anticipate patient risk.
3. Preventive healthcare applies interventions (e.g., medication reminders, follow-up visits, home care)
before complications occur.
4. Personalized healthcare tailors’ interventions to each patient’s unique profile, combining clinical,
genomic, lifestyle, and socio-economic data.
5. Over the next decade, ML has the potential to transform hospitals into learning health systems, where
each patient encounter contributes to continuous improvement in care delivery.
CONCLUSION
Hospital readmissions are one of the most acute problems of modern health care, which lead to both clinical
and economic overheads. Conventional predictive and preventive approaches to readmission have been narrow
in scope, based on statistical modeling and retrospective studies which cannot reflect the multifaceted nature of
medical, demographic, and behavioral correlates of readmission. In this regard, machine learning (ML) can be
seen as a groundbreaking tool that can improve predictive precision, customized patient care, and timely
interventions.
In this research, we have pointed out the better abilities of ML models including both the logistic regression
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and the decision trees as well as the deep learning and ensemble models. Using various sources of data, such as
electronic health records, diagnostic data, socio-economic data, and IoT-based patient monitoring, ML models
can extract hidden trends and risk factors that a clinician cannot easily notice. These insights enable healthcare
providers to make better investments in resources, minimise avoidable hospitalization, and eventually achieve
better patient outcomes.
On top of technical performance, the clinical, economic, and policy impacts of ML-driven readmission
reduction are consequential. ML allows hospitals clinically to transition to predictive and preventive care and
limit complications and care continuity. The financial cost of avoidable readmissions would reduce financial
fines levied by payers like Medicare and cut the total cost burden to healthcare systems. Politically, the use of
ML in decision-making is consistent with larger objectives of enhancing value care and health care
sustainability.
Going forward, the future of data-driven healthcare is the adoption of explainability, privacy-conscious
methods, and enabling regulations. In the process of overcoming the existing limitations and establishing
clinician trust, federated learning, explainable AI, and interoperability standards will take center stage. In the
context of healthcare systems being transformed into learning health ecosystems, machine learning will be
used not only to reduce readmissions but also drive a more general transition to predictive, preventive and
personalized medicine.
To sum up, there is more than a technology upgrade in the integration of ML into hospital readmission
reduction: it is a paradigm shift. The future of healthcare will be based on smarter, more proactive
interventions that help patients, providers, and the entire society, through a combination of advanced analytics
and patient –centered strategies.
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