The state of healthcare is changing.
Today, medical data is giving us a better understanding of how healthcare reaches the communities that need it. Recent developments have shown that even though healthcare resources are available, getting these resources to those who need these remains a huge challenge.
Medical data is deepening our understanding of people’s health. It is now clear that healthcare should be a more holistic process; one that merges behavioural health, medical science, and social services to create a patient-centric system called whole-person care.
So, how do healthcare organisations meet the challenges of whole-person care while reducing the disparity in healthcare equity? Population health analytics might be the solution you are looking for.
SAS population health analytics refers to several software modules from SAS. These modules collect data from disparate sources and convert them into useful information.
Medical data, both structured and unstructured, contains a variety of information on patient demographics.
A quick glance through the body of medical data reveals useful information, like patient age, their address, sickness, and treatment. On its own, the information is of limited value, but by using SAS analytics, you understand the healthcare needs of entire communities. Healthcare analytics helps you grasp the state of healthcare and better understand its structural problems, at least in terms of supply.
SAS also helps you gain insights at all levels, including entire population segments, helping you put the right healthcare programmes in place. In the long run, this reduces inequality and makes whole-person care a more achievable goal.
One of the biggest problems you might be facing is analysing structured and unstructured medical data coming from different sources, like scans and Fitbit devices.
Merging data from disparate sources and different formats is a trying task for any organisation, no matter what its network infrastructure is like.
SAS analytics can help you work around this problem through machine learning technology.
Using SAS analytics, you can combine health data, genetic data, geographical data, and even behavioural data to better understand patient demographics, income disparities, and other major factors contributing to healthcare inequality.
You can then use this information to construct more effective healthcare programmes, prioritising the communities that need healthcare more urgently.
Using the right technology, you can also forecast healthcare demand for entire communities and develop medical programmes based on these requirements. Identifying patients that are at high risk and optimising healthcare resource allocation based on this knowledge is also much easier.
Implementing whole-person care is a journey.
It is a practice that incorporates multiple sources of information that go beyond medical data. Behavioural health, location, finances, and other non-medical factors that determine a person’s health or access to resources all play a role. Making these connections can be difficult when using archaic data management systems.
Analytics can help make these connections and make whole-person care more achievable.
Population healthcare analytics from SAS can help us create a more accurate picture of healthcare services. Through the use of machine learning technology, like NLP, SAS analytics can analyse the data to provide a complete picture for patients.
Using SAS analytics, analysts can draw up charts to better understand patients based on their situation and healthcare needs. These insights allow you to chart more effective healthcare programmes for each patient.
Part of improving resource management is reducing the incident of error. Healthcare providers can make mistakes during surgeries or diagnosis. SAS analytics can reduce errors through data sharing and expand medical knowledge, deepen our understanding of medical procedures, reduce the rate of errors and improve patient safety and care.
As the industry trends towards whole-person care, you need systems that can help you become more proactive in managing healthcare resources.
SAS analytics offers you the tools you need to create proactive healthcare programmes that reduce disparities while providing whole-person care that helps individuals lead healthier lives.
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.
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.
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.
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.
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.
With over 90,000 cases and 3,000 deaths (and counting), the coronavirus has claimed its share of victims across the world. While there is no denying the gravity of the situation, it’s important to take a step back and phrase this problem in the appropriate context. You see, this is not the first time the world has dealt with a viral epidemic. In the early 2000s, the world was beset with SARS, swine flu and Ebola. In each case, global health organisations, like the WHO, curbed their spread (though a cure is still in the works). The current outbreak of Coronavirus resembles these problems, with one exception: Health analytics.
In the past, we spoke about how data analytics plays a huge role in healthcare, and it will be no exception with Coronavirus. In fact, global health officials are better placed to combat this problem compared to previous problems.
Health analytics makes it easier to track the Coronavirus as it spreads. Tracking the virus with conventional means is challenging because of the rapid rate of transmission. As some of you might know, the Coronavirus can spread through respiratory droplets like sneezing and coughing. It is even possible for people to spread the disease before the symptoms have manifested, so people could be carriers without even realising it. Fortunately, health analytics allows health organisations to monitor dispersal patterns, more so than any other technology.
Data analytics is so effective in tracking viral transmissions because it can draw information from different sources, like case reports and flight manifests, to deliver the most accurate readings. This is possible because health officials have access to more data than ever before, the world is producing more data than previous decades, providing millions of data points for officials to work with. Tracking the virus is the first step to understanding how it spreads so that it is easier to warn the public or take proactive steps in stopping its spread.
Building on the advantages of tracking the virus, public health officials can predict where the virus will spread before it happens. In an age where people can cross the ocean in hours, an epidemic can become a global disaster within days. It explains the large number of cases across the world – by the time public health officials realised the dangers of the virus, it had spread to most parts of China and even the world. However, health analytics makes it possible to track but also predict where the virus will be before it makes it to a location. Health analytics has proven to be a huge difference-maker in predicting the rate of transmission and direction of the virus. Predictive analytics can leverage the data collected to anticipate its trajectory.
Predictive analytics allows public health officials to get a better understanding of where the virus will spread, making it easier to develop more proactive measures. With a cure still in the works, health officials need to allocate resources to minimise the damage the virus can do. Predictive health analytics allows officials to be smarter and more concise about their measures to prevent corona from spreading, as well as allocating the resources needed to make it happen.
Health analytics makes it easier to learn more about diseases than ever before. One of the most perturbing things about the Coronavirus is that there is no known record of it in history, which means it’s brand new (meaning there is no cure). But data analytics makes it easier to study the disease because it leverages machine learning and AI to analyse the millions of data points that generate useful information on the disease. The more we learn about the disease, the better we understand the causes and symptoms of the Coronavirus.
History is rife with moments when epidemics have ravaged the population, dating back to the Medieval Ages. While the dangers of the Coronavirus should not be underestimated, global health organisations are far better equipped to combat this problem than they were in decades in the past. Health analytics has made a huge difference, as health officials find ways to combat the virus. A cure is a long way off, but analytics is giving officials other options to curb the disease’s spread in unique ways. For example, health officials can engage in syndromic surveillance using social media (and soon IoT) to monitor various health indicators from both the public and private. Data health analytics could be the key to fighting the spread of the Coronavirus until a cure is found.