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.
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.
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.
Do you believe that the application of new technologies in healthcare is not ad hoc and is necessary for the advancements in the many applications the sector offers? Well, you are not wrong. There is so much that these new findings could contribute to analyse models and unstructured data in finding answers that have long been hidden within the walls of healthcare. Difficulties that could simply be answered by using the right algorithms together with predictive analytics.
In an era where data has become the new oil, it is paramount to have the right techniques and tools for processing what is collected. Mainly because information extracted by correlation of data comes with a lot of valuable insights that could help make powerful, life-changing decisions. Imagine how it would be if two data sets having no straightforward connections were analysed together to give an absolutely miraculous finding? That’s right, this has become possible today thanks to the innovative technologies that have bolstered the many different industries across the world. And healthcare is one such industry that has immensely benefited.
Healthcare is known to be one of the richest data pools that collect its data from many different sources. But what’s important to understand here is what we need all this data for, and what challenges, opportunities and threats healthcare is posed with.
Ageing, for instance, is one of the biggest challenges the industry is facing right now. It has created the need to understand the different conditions the elderly population might face with the biological changes they go through. In a study on healthy ageing by a group of researchers from the Italian National Research Council, they too point out that many of these health complications in the elderly are comorbid, which has made it difficult to understand their causes without proper processing of data. And as for their solution, they explain how “a multidisciplinary ICT-based approach” could help them find out ways to prevent such chronic conditions using “unobtrusive and pervasive sensors, interactive activities, and predictive analytics”. Elements that have the potential of collecting and processing data from both structured and unstructured sources to determine patterns that are impossible to have done in a biomedical laboratory.
On the other hand is the growing population, which has become prominent in many of the developed countries, thanks to their relatively good healthcare systems. However, the challenge thus faced by the sector is the proliferation of population health data sets. Especially knowing what to include, where to source them from and how to capitalise on them while being compliant with the different data protection policies imposed. The reason why we need so much data directs us to the boundless value data could add to predictive analytics. Because gone are the days when technology was simply used to present data. Now, with predictive analytics, doctors and surgeons could easily determine the probabilities of certain chronic conditions by looking at the predictive insights presented by these new data analytic systems. But that’s not all it can do. In addition to preventing diseases, Jennifer Bresnick in her blog on 10 High-Value Use Cases for Predictive Analytics in Healthcare points out nine other different ways that predictive analytics can be used in healthcare. For example, creating a good customer experience, financial savings, and even the security of data with the help of AI.
A different but common problem that doctors around the world have long been facing was genetically-adaptive antibiotic-resistant bacteria. These drug-resistant pathogens have caused people to suffer for many years and to lose millions of dollars in ineffective medication. However, the world has come to a point where we can now determine the presence of these bacteria in a sample. Thanks to a group of researchers from Washington State University who found out the possibility with the use of machine learning and game theory. Laying the bricks to a healthier world.
When the world is spinning its way into predictive analysis, SAS integrates the use of embedded artificial intelligence (AI), image analytics and machine learning to help drive resourceful predictive information that doctors could now easily access. It has the ability to read data streamed from many health and non-health related sources, break them and sort into different sections, identify patterns in unrelated data, and present them in human-readable formats.
Data, as said above, are streamed from different sources such as electronic health records, wearables, social determinants, social media, genetics and diagnostics including Internet of Things (IoT) and Internet of Medical Things (IoMT). All that helps – largely due to their capability of real-time data streaming into the system. And to support this, SAS enables cloud computing as it increases agility and interoperability of data, which has also proved to be an effective way of cutting down data processing time by more than half of what it used to be.
SAS has, therefore, been able to help accelerate data analysis with the use of advanced technologies in a number of researches including Project Data Sphere led by Dr Howard Scher, an oncologist at Memorial Sloan Kettering. In that, they found unique patterns in blood that confirmed a treatment given for prostate cancer to be affecting the condition positively. Yet another breakthrough in this field thanks to data-driven image processing.
If you’d like to know more about how predictive analytics could benefit your organisation, speak to us.
The pharmaceutical industry brings us several benefits, including new drugs, so we can lead healthier and better lives, but the industry is not without it’s a fair share of problems. Duplicating workflow, multiple data sources and the risk associated with new drug discovery are just some of the problems the industry has to overcome in the discovery of new drugs. Fortunately, there is a solution thanks to predictive analytics software. In this blog post, I am going to explain the role of predictive analytics in drug development.
The current challenges in developing new drugs
To appreciate what predictive analytics software brings to the table, we need to take a look at some of the challenges the industry faces. Developing new drugs is not an easy process. Medical researchers have to overcome a slew of problems to make a new drug. Some of these problems include the risk associated with drug discovery, communicating with other departments and replicated workflows.
Developing new drugs comes with several risks. Some of these risks include, but are not limited to, portfolio risks (does the medicine add value?), operational risks (data management) and resource risks. Pharmaceutical companies must manage all these risks while developing a new product. The challenge is further enhanced by the rise of personalised medicine model, which more pharmaceutical companies are adopting.
How does predictive analytics software address these problems?
Predicting drug behaviour
Anticipating how drug molecules will behave is one of the most challenging aspects of drug development. In the past, researchers would have to conduct a lot of in vivo testing to find the right drug molecule combination. However, with predictive analytics software, developers can now simulate drug molecule combination using mathematical simulations. Using this method, drug researchers can start with a wide number of compound combinations and narrow down the selection over time. The end result is an easier method of drug molecule combination, one that delivers more accurate results.
Predictive analytics software makes it easier for drug researchers to work with others. Drug development often happens in a silo but, with analytics, collaboration with external partners becomes much easier. For example, some companies collect and store information on millions of compounds for possible candidate molecules. Drug researchers can cross-reference this information with their own research to predict the behaviour of newly discovered compounds. Thus, researchers have an easier time finding new drugs formulas that will succeed on the market.
Drug development often occurs in isolation, however, predictive analytics software will encourage a silos breakdown, encouraging medical research departments to work with CROs, manufacturing and sales departments in drug development. The breakdown occurs because predictive technologies need to map out trends and make recommendations to better reflect the organisation’s interest.
For example, pharmaceutical organisations will not only anticipate possible compound combinations but also anticipate trends past the point of production, like how the final product will perform in the market. However, for this to work, medical data needs to be contextualised appropriately. The need to contextualise data accurately will see medical research departments work with CROs manufacturing and sales department.
Better selection of candidates for patient trials
Patient trials in drug development are often a bureaucratic process. However, with predictive analytics software, you can improve the process tremendously by finding the best batch of candidates based on other merits besides being first in line. Furthermore, you can seek out candidates who are often underrepresented in the sample, allowing clinical researchers to get unique results by studying the biological effects of medicine on different body types. As a result of this, the trial process would become more accurate and easier to accomplish.
Enable smarter decisions
If organisations are to manage RnD funds properly, they need to make smart decisions about allocating their resources. Unfortunately, many organisations lack the proper tools to support their choices in asset allocation and financial investment. Predictive analytics software can help decision-making by providing more in-depth information about potential scenarios. With data analytics, organisations will have accurate tools to help substantiate their decision making.
The role of predictive analytics software in drug development
Predictive analytics software improves the process of drug research and development. Research companies will have an easier time conducting research and working with different departments. Furthermore, many of the uncertainties associated with developing new drugs are removed thanks to predictive analytics, improving the success rate and making the research process much more efficient.
Over the years technology and analytics giant, SAS, has supported and catered to the various needs of organisations across multiple industries. From aviation to insurance to education, and healthcare, SAS services have delivered game-changing insights to many. Data and analytics for any organisation across any segment, irrespective of size, have proven to be a vital tool.
This is especially true as organisations and their leaders continue to navigate the testy waters of the modern digital business landscape. The healthcare sector, in particular, is one segment that comprises many organisations continuously engaging with analytics and data, on a global scale. In most cases, the benefits of this endeavour are very clear.
That begs the question – what exactly is the value of leveraging analytics and SAS software services – especially in the healthcare sector? What changes have taken place? How have organisations benefited from this? In the following sections, we outline a few ways data and analytics, leveraged through avenues like SAS services, have benefit organisations in the healthcare sector.
As the number of patients a typical hospital sees increases every year, efficiency and effectiveness of treatment delivery become paramount. Analytics has played a huge role on this front – providing healthcare organisations with insights and visibility into their services that were previously unavailable.
By using the predictive analytics capabilities built into software and leveraging SAS services, like our SAS admin services, healthcare organisations can easily identify optimal treatment methods. By doing so, each individuals patient stands to benefit from having a truly tailored and analytics-based treatment strategy.
In addition to providing patients with the most optimal and personalised treatment options, hospitals and other healthcare organisations can use these boundless insights to plan staffing and resource allocation across all their sites. Only one analytics platform is capable of providing these insights and by combining this software with the very best SAS services, healthcare organisations stand to benefit in unprecedented ways.
Cost management and per-patient costs are becoming increasingly important as costs skyrocket each year. Maintaining insights over expenditure is, therefore, a priority for any healthcare organisation – at least it should be! With SAS software, organisations in the healthcare sector are now leveraging highly informative insights that have allowed them to significantly improve cost management.
Transparency and awareness of what the actual costs of delivering healthcare are a byproduct of SAS services and software. This is why many organisations that use the analytics platform often point to having a competitive advantage over other organisations. With the ability to track and monitor every single activity that takes place in a hospital, for example, managers and administrators can identify where costs are highest and how they can be mitigated.
Built-in modelling and diverse analyses allow healthcare establishments to conveniently assess cost-effectiveness and profitability through a centralised system. The result? A far more efficient and effective care delivery establishment that is set up to provide patients with the best care available.
Whether you run a healthcare establishment based on a revenue-generation model or you’re looking for cost-alleviating opportunities, SAS services combined with SAS software will prove to be game-changing.
Visualisation is one of the often touted benefits of analytics. The very first thing that comes to mind for many when the word “analytics” is mentioned is the idea of colourful graphical representations. It’s important to look beyond this though – preferably when you’re talking about SAS software.
With this powerful analytics platform in place and with the assistance of expert SAS services, organisations can leverage sleek dashboards that provide a consistent view of all organisational data. The user-friendly and highly functional interface allows users to make sense of the massive volumes of data coming through in a matter of minutes. By combining your insightful SAS software with the insights from the right SAS services, your organisation would benefit from intelligent analytics. Remember, having the right software in place is only the first step – you also need the right SAS services to help you setup your environment, administer it and leverage the right hosting solution (9.4 or Viya).
If you’d like to know more about our SAS installation, administration, and hosting services, feel free to contact us and we’d be happy to answer any questions you may have. Additionally, as an official SAS Resell Partner, we can help set you up with the right SAS software for your establishment. In the meantime, be sure to stay tuned to this feed for the latest SAS and analytics-related news!