Mortality, Morbidity, and Severity of Illness (SOI): Data to Support Quality Improvement  

There is a difference between mortality and morbidity and knowing the difference is essential for coders.

Often, when used in the healthcare environment, the terms “mortality,” “morbidity,” and “severity of illness (SOI)” are interwoven. However, by definition, they are quite different. Those within the healthcare industry need to understand the difference between these terms, the importance of accurately identifying each within data records, and the reasons they continue to be at the forefront of capturing accurate data and translating it into meaningful information. 

The demographics measured for morbidity and mortality among patients are somewhat the same. Morbidity includes all unhealthy patients within a designated population; mortality references the number of deaths occurring within a population. Intervention, as well as prevention, are critical factors to the significance of capturing these indicators.

Morbidity scoring indicates the severity of illness in a patient and the need for predicting successful medical inventions. Mortality may also be scored or predicted and can be used to help in clinical decision-making. There are several scoring systems used throughout the world, offering insight in predicting mortality with certain conditions. The ability to predict mortality outcomes allows healthcare providers to make continuous improvement in treatment plans, protocols, and clinical pathways. Capturing accurate information offers insight into comparing patient outcomes among healthcare organizations.

Clearly, with the advancements in medical treatments, morbidity and mortality rates may change over time. For example, with the initial onset of HIV/AIDS, the morbidity and mortality rates were both high. As a result of advancements in treatment and early detection, both rates have decreased. However, in third-world countries with limited access to treatment, the mortality rate continues to be high. 

By convention, the severity of illness is defined as the extent of physiological decompensation or organ system loss of function. Since significant functional status morbidity occurs approximately twice as frequently as mortality, using morbidity as an outcome increases the sample size and improves the relevance of big data and meaningful information.

As healthcare organizations continue to use comparative data to measure their performance and compare it against their peer groups, it has become standard practice for prospective mortality reviews to occur as a matter of course. Mortality rates are adjusted for patient characteristics that may increase the chances of death: for example, gender, socioeconomic status, and palliative care, to name a few.

Healthcare organizations must capture all of the risk information accurately, especially as it relates to the mortality report. As the industry moves more toward a consumer-based market, individuals can access information that enables them to determine the hospital that seems to have the best care. Hospitals with lower mortality rates will impact healthcare decisions made by the consumer. 

Understanding the basics regarding morbidity, mortality, and SOI is imperative for coders, clinical documentation improvement (CDI) specialists, and healthcare providers. “Big data” translates into meaningful information for the consumer as well as the healthcare market as a whole. It can be as basic as determining which hospital a patient should receive an elective surgery at, or as complex as measuring outcomes of treatment patterns or new medical technologies.

It is all about the data, documentation, and the understanding of the use of mortality, morbidity, and SOI. These elements provide a solid foundation of the fundamentals of measurable data elements that are determining factors on many decisions within healthcare.  

Programming Note:

Listen to Susan Gatehouse’s report this story live today during Talk Ten Tuesday, 10-10:30 a.m. EST.


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