The risk of chronic diseases: Identifying at-risk patients through advanced analytics
The healthcare sector is gradually adopting advanced analytics to identify patients with a high risk of chronic diseases, focusing on population health management (PHM) and adopting strategies for widespread, proactive intervention and treatment. The Affordable Care Act is moving the healthcare sector in the direction of pay-for-performance, providing incentives based on clinical outcomes. This is against the earlier model of fee-for-service and episodic payments.
PHM poses a new set of challenges to the hospital and physician community. First, how does the hospital manage clinical outcomes in a way that maximizes reimbursements without compromising the quality of care? Second, how does one identify patients at higher risk in a population and target them for special attention?
In any patient population, there will be a segment with a risk of chronic diseases. The imperative in PHM is to identify them early, intervene aggressively with treatment regimens and ensure that their conditions do not deteriorate. A major goal of a hospital's PHM program is to keep readmissions under control. In the current pay-for-performance model, this reduces revenue leakage and also eliminates penalties from the Centers for Medicare & Medicaid Services (CMS) for exceeding readmissions thresholds.
So the key question is: How does a hospital identify patients at risk of chronic diseases? This is where analytics comes into the picture. In the traditional model, most organizations used analytics to examine financial and operational performance. There really was no incentive for analysis of clinical outcomes because every encounter and procedure was billed to the payer, regardless of medical necessity or outcome.
In the new model, advanced analytical tools use a combination of statistical analysis and clinical insight so doctors can identify factors that influence risk and focus their interventions accordingly.
The benefits of advanced analytics
Among organizations that have implemented advanced analytics, 82 percent reported an improvement in patient care and 63 percent have seen a reduction in readmission rates, a recent CDW Healthcare survey found. Advanced analytics platforms are now focusing on identifying, integrating and analyzing data from a variety of sources. Many of these platforms have predictive modeling capabilities that can provide doctors with a set of inputs to target patient populations in an informed manner.
Using analytics to address chronic disease management
The CMS has identified several chronic conditions such as diabetes, acute myocardial infarction, pneumonia and heart failure as high priorities. They aim to slow the rise of healthcare costs, specifically those associated with hospital readmissions. Some of the factors that influence the risk of developing chronic conditions are medical history, demographic or socio-economic profile and comorbidities. Here's a brief glimpse at each of these metrics:
- The medical history of a patient includes standard medical data such as age, blood pressure, blood glucose, cholesterol levels and family history of chronic conditions. Statistical models use this data to analyze the progression of diseases and predict possible outcomes, which enables doctors to determine treatment.
- Socio-economic and demographic factors play a big role in health. Factors such as ethnicity, income,education levels and even residential ZIP codes provide clues to identifying patients at risk of chronic diseases. This data is not necessarily available in a hospital, but may be gleaned from multiple sources such as credit history, tax returns and shopping habits.
- Finally, patient medical risk is a function of the multiple medical conditions that an individual may have. A patient being treated for diabetes may have hypertension and high cholesterol, and may be on multiple medications to treat them all. These comorbidities have an impact on treatment decisions.
We are in the very early stages of the analytics revolution in medicine, but healthcare organizations are already identifying patients at risk of chronic diseases by leveraging advanced analytical tools and technologies.
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