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The tech revolution isn’t just on the horizon. It’s here.
Imagine data, numbers, and machine learning taking over patient care, boosting efficiency, and enhancing the decision-making process.
With predictive analytics leading the charge, we are no longer stuck waiting — we are predicting, preventing, and proactively managing health outcomes and resources.
This article gets into the essence of predictive modeling by observing its benefits and challenges in the healthcare sector and explores several practical cases.
With the help of historical data, predictive modeling enables the prediction of future outcomes. In healthcare, these models are used to expect anything from defining patients who are at risk of developing a chronic condition to which proteins are indicative of a biological function. A wide range of statistical algorithms and machine learning techniques are used to create predictive models.
Predictive modeling is critical in healthcare, impacting patient outcomes, resource allocation, and operational efficiency.
By leveraging data from EHRs (Electronic Health Record Systems), wearable technology, and other sources, healthcare professionals can predict health trends, identify risks early, and significantly improve patient care. The result is an entirely new level of proactive care, enhanced patient experience, and an improved overall business model for patient care.
Healthcare brings massive potential for predictive modeling, but its implementation faces some substantial hurdles.
The diversity and complexity of healthcare data calls for sophisticated analysis methods. Hence, data quality and data integration can make predictions unreliable.
Even if the data were perfect, privacy-security concerns make everything more complex.
Interoperability between different systems makes the situation even more challenging. Fostering an environment where information can flow easily between different systems definitely isn’t a walk in the park.
The application of predictive modeling in healthcare is a common use case when it is aimed at supporting decision-making based on data. Below are five specific subdomains where it can be used.
Risk and population health predictive models are used to identify individuals and groups at high risk of developing conditions such as hypertension, diabetes, or cardiovascular disease. Healthcare providers can use such predictive models to classify patients based on their risk level and implement preventive interventions such as lifestyle modifications, medication adherence programs, and frequent monitoring. This helps to postpone or avoid the onset or advancement of prevalent chronic conditions and improve patient outcomes, reduce mortality and morbidity, and, for the healthcare ecosystem, potentially reduce costs.
In the same vein, predictive models can use patient data (medical records, diagnostic tests, and genetic information) to find early signs of diseases such as cancer. By identifying subtle patterns or anomalies in the data, predictive algorithms can be used to diagnose diseases in early stages, when they are most treatable.
Clustering, association, and classification are crucial when applied in the healthcare industry. EDs face substantial challenges in patient flow management and resource allocation (e.g., staff, beds, medical devices, and equipment).
Here is when these techniques may be developed to forecast patient arrivals, arrivals’ acuity levels, and expected length of stay in the ED. This, in turn, can help administrators forecast demand for staff in general and allocate staff to work specific shifts.
By applying predictive modeling for safety assessment in drug development, pharmaceutical companies can identify potential adverse events associated with new drug candidates during the preclinical and clinical trial phases. Predictive models developed from drug molecular structures, pharmacological properties, and patient historical adverse event data can predict adverse drug reactions to guide drug development and improve the safety profile and reduce patient risk.
Predictive models can be used to develop tailored treatment programs for patients based on their unique characteristics, such as genetic makeup, biomarker profiles, and past treatment response history. Models constructed from different data sources (i.e., data silos), such as those from clinical trials, real-world evidence, or patient health records, can assist in identifying effective treatments for each patient.
As healthcare organizations face a rapidly approaching future, they must start implementing a clear and thoughtful action strategy for rising to the challenges of predictive modeling.
Focusing on improving data quality will allow healthcare specialists to maximize positive outcomes.
Additionally, involving all necessary parties in the common goal will ensure that predictive models are applied properly and in line with the organization’s needs.
Finally, keeping the systems user-friendly will increase the adoption rate and improve the overall implementation process.
Kanda Software can help you at every stage of adopting predictive modeling for your business. Our team of experts in AI and ML develops custom solutions for companies of all sizes across various domains.
We’ll help you create the environment you need, provide the guidance you need to manage and process your data step-by-step and enhance the efficiency of your medical practices.
Contact us to get started, and let’s shape the future of intelligent healthcare management together.
