Optimising SAS health analytics for better outcomes
SAS health analytics is the go-to tool for many hospitals and healthcare organisations when it comes to improving health outcomes. However, are we helping them make the most out of their investment? Medical data is vast and varied, covering everything from electronic health records to administrative data.
As SAS engineers and technicians, are we helping health organisations make the most of their data so that they achieve their objectives? Drawing from my own experiences, healthcare organisations focus on a handful of key areas to improve operations.
Optimising their healthcare analytics platform for these areas will improve outcomes tremendously, and optimal outcomes in healthcare are more important than ever before to combat modern diseases, like the coronavirus.
Areas to look at when optimising SAS health analytics
The needs of hospitals and other healthcare organisations are always evolving. Once the analytics environment has been set up and the data connected, organisations make plans for smarter, more sophisticated analytics capabilities. This often means progressing from descriptive analytics to more advanced systems like predictive analytics. In other words, they want to upgrade – and quickly.
Healthcare organisations are making this move because the true competitive advantages lie with more advanced capabilities. As SAS professionals, we need to optimise SAS healthcare analytics to make the transition as smooth and efficient as possible.
This often means making it easy to expand the functionality of SAS health analytics and making the environment more accessible to people at all levels, so that they too can contribute to data analysis.
Breaking down silos
As healthcare organisations become more digitised, they are producing a wealth of data. However, most current systems isolate data, preventing data analysts from drawing full value from the information. Healthcare organisations are coming to realise that there is more to gain from breaking down the silos in data systems.
We see the importance of collaboration in SAS health analytics when it comes to combating the spread of the COVID-19 pandemic. To have any chance of combatting the virus, people with an understanding of AI, biology and population models need to work together with data analytics.
Optimising SAS health analytics can make this cross-collaboration easier. Perhaps, some health care organisations want to improve collaboration between different departments within the company itself, but in some cases, healthcare needs to work with different industries, altogether.
Whatever their needs are, we need to optimise SAS healthcare analytics to facilitate the sharing of data and bust silos in storage.
Cutting down operational costs
Healthcare organisations, like hospitals and private companies, are looking to optimise their operational costs and are turning to SAS health analytics to make it a reality. But how are they making operations more efficient?
One option is future-proofing investments. For example, before setting up in a new region, healthcare organisations can do workforce mapping to match resources with the potential population in the area, making it easier to plan out capital expenses.
Furthermore, they can anticipate the demand for services, making it easier to allocate resources more efficiently. Optimising SAS health analytics to assess operational costs allows for better use of resources, cohesive capital planning and coordination amongst different departments.
One of the most important objectives for hospitals is to create a system for personalised patient care. In fact, you can say that previously mentioned objectives, like breaking down silos, are in service to the goal of providing personalised treatment for patients.
As analytics engineers, optimising SAS health analytics to provide more patient-centred care should be one of our top priorities. But how do we make that happen?
To provide personalised care, doctors and nurses need to be updated on a client’s specific condition, their stage of treatment and other unique requirements. Healthcare analytics systems can help in several ways because machine learning algorithms and AI can draw data from outside sources to give healthcare organisations more information.
Predictive analytics can be used to predict oncoming occurrences or willingness to engage in treatment. Furthermore, analytics can draw information from different sources to segment patients based on any criteria that the hospital needs.
What’s the ultimate goal for hospitals with data analytics?
Healthcare organisations are looking for an advanced system that can help them draw the most value out of their biggest asset: Data. In fact, you can say that data analytics is more important than before because of what is happening with the pandemic.
As SAS professionals, optimising SAS health analytics to meet the needs of hospitals and healthcare organisations should be one of our top priorities.