The NMDS is a set of patient health information collected on everyone who visits a health organisation in New Zealand. The information is anonymised and predominantly used for small scale administration and auditing. For this study, we utilised the records of all patients in NZ who had undergone surgery in 2013-2014 as our data set. The two-fold goal of analysing this data is firstly, to assess and quantify long-term surgical risks and secondly, to provide clinicians and patients with accurate risk assessments.
One of the major areas of risk being assessed is the increased rates of morbidity after a major surgery. Obviously, the intention of surgery is to save lives and overwhelmingly it does; under certain circumstances however, complications caused by the bodily trauma of surgery can be related to pre-mature death, largely due to surgery exacerbating pre-existing health issues. The focus of this study is trying to find which factors have an impact on mortality, and also give clinicians an accurate risk assessment.
By analysing the secondary data of patients such as additional but not illness-related health factors and demographic data, it’s possible to illustrate how post-surgery morbidity rates may have much more to do with pre-existing conditions and long-term lifestyle factors than the surgery itself. This study specifically aims to separate mortality associated with surgery from background mortality, quantify this mortality, and then predict which operations have the highest risks associated with them.
This allows us to factor in, and create complex risk assessment profiles based on factors such as a patient’s age, ethnicity, gender, diagnostic acuity, the complexity of the surgical procedure and many other factors. Not only does this enable better long-term analysis for individual patients, it also allows clinical researchers to identify highly specific, cross-demographic groups that have higher or in some cases lower risks.
The major benefit of this type of data analysis allows clinical researchers and care providers to provide far more practical, efficient and essential health services based on where they’re needed most. A major benefit of this is making our risk profiling more accurate for NZ public health needs by using NZ-specific data, however this idea could be used in any country with electronic health records and serves as an example of precise healthcare being tailored to individuals. Not only can this help reduce rates of post-surgical mortality, it can also influence public health policy and planning by accurately identifying and even predicting areas of differing or potential risk in the public health sector.
To learn more about the NMDS and how it’s being used by clinical researchers, check out the seminar video at this link.