With over 130,000 COVID-19 cases in Australia, the healthcare industry has been working diligently to find new ways to curb the spread of the disease and ensure better health for the population.
As a result, the healthcare industry has become more reliant on data analytics than ever before.
While data analytics has always played some part in the healthcare industry, after the COVID-19 pandemic, the data analytics landscape for the healthcare industry has broadened, and new avenues for big data analytics have come to light.
The pandemic has resulted in a dynamic environment that keeps delivering new revelations related to the pandemic and a multitude of new healthcare options for keeping people safe. Healthcare data analytics models have to change and adapt rapidly to keep up with this dynamic environment.
In this post, we explore how healthcare data analytics has changed post-COVID-19 and what this could mean for the future.
The healthcare industry has become increasingly reliant on the use of IoT technologies such as wearable sensors and monitors that help keep track of COVID-19 patients and to monitor the health of individuals who are suspected of having the disease.
These devices collect and transmit an ocean of data, which—with the help of data analytics—healthcare professionals can use to gain insights that help identify areas of improvement in healthcare facilities
For instance, with the help of advanced algorithms and artificial intelligence, medical professionals can have better insights into the logistics involved in deciding which patients need treatment more urgently and determining the most effective ways to treat them.
Businesses across industries quickly realised that the war against COVID-19 can’t be won by fighting alone.
As a result, many industries formed alliances to find solutions to bring the effects of the pandemic under control. The health industry itself started working with organisations from different industries for this very reason.
For example, by partnering with a virtual drug discovery platform provider, healthcare professionals and institutes like Harvard Medical School were able to use data analytics to compare the efficacy of drugs against COVID-19 proteins, which helped find new treatment options.
With these collaborations, the healthcare industry is receiving large amounts of data, which can fill the gaps in their understanding of the current pandemic situation and future approaches to healthcare.
The pandemic had made it clear how critical collaborations are for the healthcare industry to leverage its data analytics capabilities.
Telehealth was offered as a convenient alternative to traditional healthcare systems, allowing people to connect with medical specialists remotely.
Today, telehealth has become a common standard due to social distancing laws. Even in a post-COVID scenario, telehealth is used by many people because of its convenience.
Due to this, there is an urgent need to improve the capabilities of telehealth platforms, and Big data analytics has become a crucial tool in this process.
Healthcare analytics systems use big data to analyse patient information for a more accurate diagnosis.
Big data can also help improve communication between telehealth providers and patients, making telehealth more intuitive and user-friendly.
Data analytics once played a moderate role in healthcare, but post-COVID, it has evolved and opened new opportunities for improving treatments, diagnosis, and relationships with patients.
If you work in the healthcare industry and are looking to leverage your data analytics capabilities, our Selerity analytics desktop is what you’re looking for.
This is the ultimate platform for managing your SAS ecosystem and enhancing your SAS experience.
Get in touch with the Selerity team for more information.
COVID-19 is the biggest public health crisis the world has seen since the Spanish Flu, which infected one-third of the world’s population in the aftermath of World War I, and killed more than 50 million people.
Even now, more than 18 months after the first COVID-19 patient was detected, governments, hospitals and other healthcare institutes are still struggling to contain the spread of the pandemic.
Fortunately for us, there is one distinctive difference between the Spanish Flu and COVID-19—the pace at which pharmaceutical companies have been able to develop vaccines.
Another contributing factor to the success and speed of the COVID-19 vaccine development was data analytics in healthcare.
Thanks to these advancements, which have reduced the typical vaccine development timeline of 10 years, pharmaceutical companies have been able to explore, develop, trial and receive approval for multiple vaccines within two years since the outbreak of the virus.
It has also helped government authorities in developing and implementing successful vaccine rollout plans to inoculate as many people as possible.
In this post, we take a deeper dive into the role data analytics played in the development, manufacturing and distribution of COVID-19 vaccines.
The development of a vaccine for any disease requires pharmaceutical companies to conduct rigorous experiments and clinical trials involving a large number of participants, to prove the efficacy of a vaccine against a virus.
The typical vaccine development process takes a minimum of 10 years before the vaccine gets approved by regulatory authorities in charge of widespread manufacturing and distribution.
The need of the hour, however, forced the pharmaceutical industry to adopt novel techniques in both analysing the data at hand and developing vaccines and experiments to speed up the development process.
One such data analytic tool used in the development of the COVID-19 vaccine is Design Of Experiments (DOE), which improved the efficiency of preclinical and clinical experiments while reducing the number of experiments otherwise required.
DOE ultimately helped companies design and approach experiments systematically, allowing them to identify and determine the effects various factors had on the outcome of clinical experiments.
The success of vaccine development also relies on how fast pharmaceutical companies can manufacture the doses required to meet demand. Scaling the manufacturing process up to meet the needs of billions of people in a very short timeframe, however, is entirely unprecedented.
Fortunately, data analytics tools like predictive analytics and multivariate analytics helped companies not only predict demand to scale production up but also to reduce the number of batches needed to prove the efficacy of the vaccine.
Vaccine distribution is perhaps the area where data analytics is most traditionally used. This time around, data science was used to predict and handle fluctuations in vaccine supply due to various political, economical and logistical factors.
Government authorities planned their vaccine rollout strategy to prioritise the most vulnerable locations and population segments. Many countries, for example, prioritised vaccinating everyone over the age of 65 as that segment of the population was deemed the most vulnerable to the virus.
Governments also used data analytics tools to detect virus transmission patterns to predict possible outbreaks and prevent them by vaccinating the people living around that geographical area.
COVID-19 is likely to be the most impactful healthcare crisis of our lives. Fortunately, things may return to the new normal sooner than we anticipate with the introduction of life-saving vaccines.
The development of these vaccines involved the adoption of many novel techniques including new vaccine production methods and data analytics to support this.
It also helped pharmaceutical companies and governments in developing, manufacturing and distributing these vaccines to protect the health of millions of people around the world.
As such, data analytics will continue to be a force for good as it helps us navigate the uncertainties of public health crises—today and in the future.
Clinical research trials are a huge problem for pharmaceutical companies. According to research, the median cost of research trials was $19 million with an interquartile range (IQR) of $12 million to $33 million. This makes research projects a very expensive proposition for most organisations (which could lead to several issues like funding).
Expenses, however, are only one problem facing clinical research organisations (CROs). Finding suitable volunteers for the trial is also a significant challenge. Research trials often fall short of the required number of volunteers. When this happens, it throws research trials off—compromising accuracy and test results.
To work around these problems, clinical research organisations (CROs) need to find new solutions to help optimise clinical research, anticipate problems, and mitigate some of the problems they are facing. One of these solutions is predictive analytics platforms.
Predictive analytics helps CROs resolve several problems related to clinical research.
Predictive analytics can help organisations improve the recruitment process. CROs are turning to analytics and treasure troves of big data to make better decisions when it comes to assessing the feasibility process. For example, rather than spending hours creating the perfect survey, patient recruitment is now a more data-driven process.
A data-driven approach provides a more detailed scenario of the possible patient base for a particular drug or treatment. By using this data, it becomes much easier to predict clusters of people that are eligible for clinical trials in a certain geographical location. This helps reduce the time taken to find a suitable list of candidates for research, making the process far more efficient than before.
Predictive analytics platforms can help CROs work around unexpected volunteer shortages. For example, if researchers usually turn to particular trial sites to find their candidates, they can use predictive analytics to check if they can get a sufficient number of patients needed to complete research trials. They can also determine if they are falling short of the required number of patient volunteers and take action to remedy the problem.
However, it is not just a question of anticipating patient volunteers for a clinical trial. It is also a question of understanding their behaviour.
Human behaviour is complex and there are many variables that are hard to anticipate during the research process. Will volunteers behave as expected? Will they drop out before the trial process is complete? Clinical research analytics can help CROs work around this problem by anticipating how patients might behave during the clinical trial process by mining medical data.
It’s not unusual for organisations to spend millions, if not billions, on research and development. The R&D process absorbs a significant amount of funds and takes years to complete before one drug is ready.
Predictive modelling platforms can help CROs reduce the cost of clinical research trials. The analytics platform can account for several variables that can affect drug development.
Predictive models can account for many variables related to the viability and efficiency of a drug. Furthermore, the model allows users to simulate different phases of the disease process on the human body (depending on the research trial), making many parts of RnD, like drug formulation and side effects, more cost-effective.
This additional insight can help researchers optimise parts of the trial process to reduce the cost of drug research and development, as well as bring the product to market faster. Researchers also have an easier time understanding complex medical data thanks to analytics.
The cost of clinical research trials is growing and it is taking longer to get the product to market.
CROs can use predictive analytics to make the process more efficient than before by helping organisations make better use of their big data. Big data is a useful asset that contains a lot of valuable information—information that can help CROs optimise the trial process.
This can help organisations reduce the cost of research trials and shorten the time required to bring the product to fruition, which will help improve ROI on medical research.
To learn more about predictive analytics platforms, visit Selerity.
Data analytics solutions are crucial for providing value-based healthcare. However, there is one kerfuffle to using them: the quality of insight depends on the data analysed.
If the quality of data is poor or biased, then data analytics will yield unreliable results. Therefore, data analytics strategies should clean data, and improve its quality. Better quality data means more accurate readings, which increases the value of healthcare. Fortunately, healthcare organisations can use several strategies to clean the data and maximise value on analytics.
In this blog, we explore different strategies to improve the quality of healthcare data.
Here are some strategies we could use to maximise data analytics in healthcare.
With medical organisations relying on data analytics in healthcare to drive decision-making, it is important to ensure that there is no bias in data. However, accurate, bias-free data is incredibly challenging to create because the process is rather complex. Bias covers different areas, like gender, race and location, making it difficult to fix.
One option is to have data analytics and medical professionals work together on the creation of data analytics tools in healthcare. This way, data analytics engineers know what is important for the healthcare provider. Meanwhile, the provider can make more informed decisions because they know how the analytics platform works. This gives them more confidence and agency in decision-making.
However, creating the right data analysis tools is just one step. This is because data collection tools should also be bias-free to ensure results are free of an implicit or explicit slant.
Data analytics in healthcare thrives on a rich dataset. However, accessing a varied dataset is not always possible due to issues, like data privacy. Normally, machine learning models need a centralised database with all required data in a single source.
Due to legal and practical reasons, compiling data into a single source is not possible. For example, privacy concerns prevent healthcare organisations from using data in this way. Furthermore, data might not be useful for addressing certain scenarios, like local health issues.
To work around this problem, data analytics professionals are using federated learning. This method allows algorithms to access data across several devices or servers. Federated learning allows healthcare professionals to access local data without violating privacy laws.
While it is common knowledge that data analytics platforms benefit from exposure to rich, varied data, it is not always possible for analytics personnel to conduct this practice. This is because obtaining quality training data is challenging. The current crisis has brought this shortcoming into sharp focus. Healthcare researchers need to understand how the Coronavirus works to create treatment programs.
Data analytics in healthcare can get through the vast literature to help optimise the process. However, for that to work, data analytics platforms must be nurtured on a steady diet of raw data to ensure accurate results. For example, healthcare professionals are using AI to refine the quality of training data. Automated systems can scan medical images to get more data than conventional technology could. By using this new technology, healthcare professionals create a large dataset to train data analytics platforms in healthcare.
Automated technology can also optimise the data collection and analysis process. For example, AI technology can scan hundreds of thousands of articles on a medical condition to help healthcare professionals find useful information that aids treatment.
However, AI tools can do more than prep data analytics tools. They can also help healthcare providers. For example, AI can help providers manage workloads to make them more sustainable, like aiding clinical decision-making at the point-of-care.
The key to creating an efficient healthcare system is to use data analytics in healthcare to its full potential. However, analytics platforms must be trained with the right dataset. Therefore, creating strategies to maximise the quality and value of data is crucial for improving the healthcare process.
While there is much to gain from investing in data analytics platforms, the investment wouldn’t amount to much if datasets are not complete or lack the quality required.
When collection and analysis are properly optimised, it allows organisations to make the most out of their medical data and analytics platforms. To know more about analytics platforms for the healthcare industry, check out the SAS solution on data analytics for healthcare.
Healthcare analytics is paving the way for better treatment for cancer. In Australia, 150,000 people were diagnosed with cancer. In 2019, cancer was the leading cause of death amongst Australians. Fortunately, healthcare providers can combat cancer more effectively, thanks to data analytics. In this blog, we are going to explore some of the reasons why cancer is so hard to combat and how healthcare analytics can rectify the situation.
One reason why cancer is difficult to combat is because of its pervasiveness. As the leading cause of death in Australia, it has claimed thousands of lives and afflicted many more. Even in the US, cancer is a prominent chronic disease with over 442.2 per 100,000 men and women contracting it.
However, there is also the issue of treating the disease. Treating cancer is a complex process, it requires a lot of data that human medical personnel have difficulty processing. For starters, cancer is not a single disease but hundreds of different types of ailments. There are also other factors at play, like patient lifestyle, physiology, attitude, and tumor physiology. The complex nature of cancer treatment requires a lot of data.
There is also the issue of refining the treatment process. Cell engineering and cancer treatment research have been growing. However, actioning these findings have proven to be very difficult.
Furthermore, traditional tools don’t always meet the requirements. For example, conventional risk assessment tools don’t have the detail healthcare providers need. The gap between the information required and what is available is one of the main reasons why cancer treatment is challenging. This is where healthcare analytics becomes an asset.
Healthcare analytics can help healthcare providers in different ways.
Healthcare analytics provides detailed readings on cancer treatment and prevention. Data analytics platforms incorporate data sources, like family and hormonal reproductive history. So healthcare providers get more information on cancer prevention and treatment. Machine learning models can forecast patient risk better than traditional risk assessment platforms. Meanwhile, traditional models only consider a narrow selection of factors, making treatment and prevention a struggle.
Healthcare analytics can help healthcare providers conduct more precise cancer treatments. Most cancer treatments come down to two options: “Block” cancer cells or “kill” them. Healthcare analytics can help healthcare providers be more precise in their treatment.
Analytics platforms can analyse medical databases to better understand protein strands. Healthcare providers can use the insight to deepen their understanding of cancer cells.
Data analytics platforms can analyse millions of protein combinations. These protein combinations can target cancer cells without damaging normal cells. In other words, healthcare analytics can refine treatment. Thanks to data analytics, it is now possible for healthcare providers to take full advantage of the rich body of genomic data to find more precise and effective cancer treatments.
Healthcare analytics platforms can help identify cancer at its earliest stages. Identifying cancer at an early stage is key for ensuring patient survival. Data analytics platforms can help identify the chances of a patient contracting cancer by analysing data from different sources. Furthermore, analytics platforms make it easier to discover data trends by visualising data findings, making it easier to draw connections between disparate data sources.
Data analytics platforms can help healthcare organisations improve the pace and rate at which treatment is taking place. Healthcare analytics can help organisations optimise patient and treatment scheduling, ensuring that patients get better treatment and care, improving the patient experience better for those dealing with this difficult affliction.
Cancer is a serious ailment that has claimed thousands of people and continues to do so as we speak. While there has been groundbreaking research in this field, taking those findings and converting them into proper treatment procedures remains an issue. Healthcare analytics allows providers to close the gap between research and insights to make cancer treatment more precise. By using data analytics, healthcare providers will be better equipped to combat this serious ailment that has claimed thousands of people in Australia and around the world.
To learn more about healthcare analytics, visit Selerity.
COVID-19 has reshaped the way humans interact with technology in healthcare. Intelligent data, technology, artificial intelligence (AI) and machine learning (ML) have started to lighten the burden and establish new ways to facilitate sustainable demand and supply. But at the helm of this transformation are predictive analytics software tools.
Predictive analytics in healthcare is rapidly becoming some of the most-discussed in healthcare analytics. The enormous amount of data the pandemic generated has given researchers and providers the opportunity to analyse trends, monitor patient populations, and begin to rectify long standing issues in the healthcare industry.
The ability to anticipate future events is critical in the midst of a healthcare crisis, and predictive analytics software tools can help healthcare entities do that. They are helping healthcare organisations stay ahead of poor outcomes, resource shortages, and other regressive impacts of COVID-19.
Furthermore, organisations are turning to predictive models to better understand which patients are at risk, where resources are most needed, and where the disease is likely to spike next. To further understand the true impact of predictive analytics models, let us dive straight into the potential benefits to boost our fight against the pandemic.
Determining which patients are most at risk for contracting the virus – and which individuals are likely to experience poor outcomes from COVID-19 – is perhaps the most important use case for predictive analytics during the pandemic.
Researchers and data analysts have worked to uncover the factors that may influence disease susceptibility and severity, aiming to get ahead of surges in cases, hospitalisations, and deaths.
Applying machine learning approaches to data from a large cohort of patients with COVID-19 resulted in the identification of accurate prediction models for mortality. The ability to accurately predict whether or not a patient is likely to test positive for COVID-19, as well as potential outcomes including disease severity and hospitalisation, will be paramount in effectively managing our resources and triaging care.
As we continue to battle this pandemic and healthcare professionals prepare for a potential second wave, understanding a person’s risk is the first step in potential care and treatment planning.
With the rapid spread of COVID-19, many hospitals and health systems are faced with the possibility of sudden surges in patient volume, resulting in limited resources, and increased burden on staff.
To better plan for these potential surges, organisations have implemented predictive tools to help allocate resources. With predictive analytics software tools, you can match the daily demand of hospitals with their necessary resources i.e beds, staff, PPE and other supplies.
Predictive analytics software tools provide timely, reliable information to forecast patient volume, bed capacity, and ventilator availability to optimise care delivery for COVID-19 and other patients. These platforms can track local hospitalisation volumes and the rate of confirmed COVID-19 cases. Furthermore, these models can help anticipate and prepare for increasing COVID-19 patient volumes with 85 per cent to 95 per cent degree of accuracy by running multiple forecasting models.
In a situation as unpredictable as a global health crisis, having some idea of which areas will be most impacted can help public health officials and providers get ahead of poor outcomes.
Using predictive analytics software tools, researchers can find patterns and trends in different places that can inform social distancing guidelines and other preventative measures.
Currently, hospitals and governments have developed several predictive analytics models to examine COVID-19 trends and patterns around the world. Researchers believe that past data encodes all the necessary information.
Predictive models could also demonstrate the importance of other COVID-19 regulations, like wearing masks.
As the pandemic continues on, predictive analytics will continue to play a significant role in monitoring the impact of the virus, from patient outcomes to areas of increased disease spread. The global health crisis has only further highlighted the importance of predictive analytics in healthcare, and can accelerate the use of these tools in standard care.
These models can help hospitals, health care facilities, state health departments and government agencies forecast the impact of COVID-19 and prepare for the future.
Visit our website for more information on how predictive analytics software tools could revolutionise the global approach to the pandemic.
Back in the days of yore, there was no plausible alternate reality where the large amount of data churned out by the healthcare industry could be collected and analysed in real-time. With the advent of real-time analytics in healthcare, we see technology pushing the envelope on what we can truly achieve in this sphere.
The end goal in healthcare is to save lives, shorten hospital stays and build healthier communities around preventative care. How can real-time analytics help achieve these goals?
Healthcare information is disconnected and not readily accessible in a centralised, informed manner, greatly limiting the industry’s efforts to improve quality and efficiency. Real-time analytics in healthcare addresses these issues head-on by bringing disparate and siloed data from many sources into one place.
The information gained from analysing massive amounts of aggregated health data can provide actionable insight to improve operational quality and efficiency for providers and insurers alike. This increased efficiency is necessary for the healthcare industry that is rapidly transitioning from volume-based to value-based healthcare. Now more than ever, it is critical that clinicians and providers identify and address gaps in care, quality, risk, and utilisation to support improvements in clinical outcomes and financial performance.
In the age of prime technological advancement, we see a host of new gadgets that are revolutionising our healthcare experience. With virtual visits, real-time patient scheduling and AI that serves as the first point of care, we see convenience and efficiency take over systems that were mostly linked to being slow and ineffective.
Wearable devices, like necklaces and bracelets, are no longer purely for aesthetics, they also double as your lifesaver. These devices are useful tools in preventive care for patients because they measure vital signs to diagnose conditions like hypertension and asthma. This is only the surface of what real-time analytics in healthcare are capable of and publicly available to the masses. The research and development side of the healthcare system is testing out products that could change the very way we view healthcare.
Real-time analytics in healthcare has the potential to greatly impact the patient-doctor synergy. Smart devices encourage patients to be more involved in their own treatment process, empowering them to take their health into their own hands.
Through remote health monitoring, patients will have real-time visibility into their vital signs, such as blood pressure and heart rate. Not only will this information give patients insight into how their habits contribute to their health and motivate them to follow the treatment, it also allows nurses or medical officers to receive alerts should a patient’s condition change or reminders need to be scheduled.
With real-time analytics, hospitals can have a 360-degree view of the patient. Using this data, the healthcare industry can deliver proactive care, improving health outcomes, reducing hospital readmissions and improving all-round efficiency.
Real-time analytics in healthcare allows clinicians to go deeper and broader in medical services. But most clinicians are hampered in their inability to access and analyse said data. Access to patient history data is often difficult to come by, and even if the data comes through, analysing and incorporating it meaningfully into diagnosis is a challenge. However, real-time analytics in healthcare can resolve this problem.
Real-time analytics can combine insight from historical information with current data, making it easier to conduct a deeper and more comprehensive treatment than before. Naturally, both medical providers and patients benefit from the use of data analytics.
Healthcare thrives on real-time information and as the industry is further empowered by technology, a growing number of healthcare management systems are leveraging real-time analytics to give healthcare providers every single advantage possible. Superior analytics platforms enable predictive analytics of data in motion, allowing healthcare providers to capture and analyse data – all the time, just in time.
Among the benefits of big data in healthcare is the possibility to improve the quality of medical services, track financial performance and detect fraud while freeing doctors from routine work. The newfound freedom gives doctors more time to do what they have to do – help people maintain their health and anticipate unforeseen health issues in time.
Visit our website for more information on real-time analytics in healthcare.
When we think of healthcare, we think of health monitors and doctors in white coats, but we rarely think about data and analytics. Yet hospitals generate a lot of valuable data regularly. In 2018, healthcare organisations generated over 8.41 petabytes of data, which was an explosive growth of 878 per cent compared to 2016, and this number would have grown significantly in the past few years. Given the large volume of medical data these organisations have, it is important to invest in an analytics platform that can analyse data and generate the appropriate outcomes.
But what is the most appropriate health analytics system for healthcare organisations? While there are plenty of great data analytics systems out there, I truly believe that SAS healthcare software is the best choice for hospitals and organisations and by the end of this blog, you will feel the same as well.
There are several reasons why SAS healthcare is suited for medical data. Here are just some of the reasons.
Medical data is incredibly complex. I am not just referring to the volume (though that is considerable!) but also the structure and variety of data. Medical data draws from different sources like administrative data, clinical data and research data. What this means is that data draws from different sources. Patient records, research reports and MRI scans are just some of the examples of medical data. When you take into account data from medical research, then the volume and complexity balloons even more. The sheer variety of data sources means that healthcare organisations have to draw from both structured and unstructured data for their analysis needs. This is where SAS healthcare analytics comes into play.
The analytics platform is designed specifically for large datasets and can process data at rates most analytics platforms would find difficult to match. In addition to features like AI and cloud computing, the analytics infrastructure is designed in such a way to process big data efficiently. While other solutions can process data, it cannot be done at a rate that matches SAS analytics. By investing in this platform, healthcare organisations can save a lot of time without compromising the immense data volumes of the industry.
In addition to being very complex, medical data is incredibly sensitive. Healthcare organisations have to follow the regulations mandated by regulatory agencies like the FDA and the Australian Health Practitioner Regulation Agency (AHPRA). This is because medical data is incredibly sensitive, containing valuable information for clinical trials and the personal condition of patients. Hence, when processing data healthcare organisations require an analytics platform that can secure the data while breaking down the data to meet objectives. SAS healthcare software comes with the features necessary to secure data. This means healthcare organisations can analyse their data, while also following the stringent regulations that govern data collection and use.
The process of installing, administering and maintaining analytics platforms is not simple. Healthcare organisations, whether they are hospitals or medical research organisations, need to consult specialists to help them properly optimise and administer the platform. While some analytics platforms have a great community of users, they often lack the technical support that SAS healthcare analytics has. In addition to SAS experts who can install and monitor the solution over time. This means healthcare organisations don’t have to worry about maintaining the solution because a team of dedicated SAS experts will optimise their findings, saving a lot of time and effort. SAS experts can ensure that the solution is properly integrated into company systems and optimise the solution so that it generates the most accurate findings possible, without you having to get involved directly. Healthcare organisations can save a lot of time using SAS analytics.
Healthcare analytics can be a huge asset for healthcare organisations provided that they select the right analytics platform. With its ability to process large data volumes and secure sensitive information, SAS healthcare could be the perfect solution for these organisations. Now more than ever, healthcare organisations need to invest in powerful analytics technology to generate the best possible outcomes in the shortest time possible.
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.