3 Compelling Cases of AI-Driven Patient Risk Stratification Tools

High-risk patients require continuous care, and their healthcare costs can touch the skies. A report suggests that hospital expenses per patient went up by 11 percent at the end of 2021 and drug spending increased 7.7 percent. As a result, the overall healthcare expenses surged during the last couple of years.

The US government too spends much on healthcare to curb rising chronic diseases, pandemics, and epidemic situations. In fiscal year 2020, the US government spent a total of $347 billion on health, that’s $1,043 per person. Preventive measures such as patient risk stratification tools can curb healthcare costs and improve patient outcomes too.

Risk stratification identifies high-risk patients and addresses their needs early on, it also prevents the onset of serious diseases. AI-driven patient risk stratification tools can speed up the process of detecting high-, medium-, and low-risk patients. Healthcare organizations and the government can then initiate preventive health measures for them.

Benefits of Patient Risk Stratification

Some of the major benefits of patient risk stratification are:

1. Risk assessments will lead to early interventions, thus lowering hospital readmissions and better outcomes.
2. Fewer readmissions and preventive measures early on help to curb healthcare expenses.
3.  It offers insights into precision care for high-risk patients.
4. Helps to identify disease patterns in a population.
5. Target resources based on high, medium, and low-risk patients.

AI-Driven Patient Risk Stratification Tools: Case Studies

1. ML Tool for CKD Management

CKD (Chronic Kidney Disease) is a nationwide burden for the healthcare sector. A high prevalence of hypertension and diabetes is considered the main precursor of CKD. Around 11.6 percent of the US population had diabetes in 2021, and 48.1 percent of adults suffer from hypertension. The onset of CKD can be controlled through early detection, prevention, and control of hypertension and glycemia.

A research study published on the NCBI suggests that ML (Machine Learning), a branch of AI, can be used to stratify CKD patients. Using machine learning algorithms for early detection of high-risk CKD patients is proved to be more cost-effective and efficient than traditional population-based screening methods, as per the study.

Furthermore, the research study signifies that risk stratification can be associated with a decrease in the number of people who need close monitoring of the eGFR (Glomerular Filtration Rate). Thus, a positive impact is expected from patients suffering from CKD by using machine learning algorithms for patient risk stratification.

2. ML-Based Risk Stratification Tool for In-Hospital Mortality

A research was published in the Journal of Translational Medicine that talked about using new machine learning algorithms for creating a risk stratification tool that correlated patients’ in-hospital mortality and clinical features. Researchers used a gradient-boosting algorithm to build a model that predicts the mortality risk of heart failures of patients in the ICU.

The research indicated that the performance of the ML or other AI-driven patient risk stratification tools was superior to the conventional risk predictive models. The machine learning risk stratification tool could monitor patients’ clinical data-specific cardiovascular biomarkers. The tool can support clinicians in assessing heart failure patients in intensive care units and making personal treatment plans. However, this AI-driven patient risk stratification tool needs to be validated in the study of independent cohort studies.

3. Patient Risk Stratification Tool for CHD

AI algorithms are known to analyze surplus data, based on which patients are segregated as per their risk levels. AI models are transforming CHD (Congenital Heart Disease) through analysis of huge datasets. This approach is set to change the way clinicians approach diagnosis, interventions, and treatment for CHD patients. Moreover, using risk scores based on AI predictions can augment care for CHD patients. AI learning models can aid with diagnosing fetal congenital heart defects via imaging.

Research published on the Cureus site states that AI algorithms make it easier to collect data from wearables to monitor ambulatory health and assess risk for CHD. The study further elaborates that machine learning models can predict outcomes such as length of stay in the hospital, time on the ventilator, and mortality following a congenital heart operation.

Intrinsic machine learning approaches make it possible to accurately predict post-operative complications and the factors that influence post-operative complications. With AI predicting a majority of CHD aspects, it becomes easier for healthcare organizations to segregate patients based on the CHD risk levels.

Challenges Associated with Risk Stratification

The following are some possible dangers connected to the use of risk stratification techniques in patient care:

1. Patients who are not at high risk may be mistakenly categorized as such. This may cause the patients concerned to undergo needless interventions and experience worry.
2. If personal data is not stored securely, there is a chance that it will be misused.
3. Healthcare professionals may misinterpret risk scores. This could result in issues with prognosis, diagnosis, and therapy.
4. Some patient groups may unjustly be singled out for assistance while others are not due to provider bias.
5. It’s possible that some of the data sources utilized to stratify patient risk are not precise or current. Some of them might be predicated on information gathered years ago.

Future of Risk Stratification Tools

Improved AI-driven risk stratification models will enable healthcare professionals to anticipate, rank, and stop the course of disease to improve patient outcomes and their overall quality of life. Patient risk stratification in the future will also require larger data sets with more predictor variables than it does now.

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