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Hire NLP ExpertsOne study estimates that the human body can generate data amounting to 2 terabytes every day. This data may include activities related to the brain, heart, stress, sugar levels, and more. But to handle such copious amounts of data, advanced technologies are needed, and this is where public health and data science converge.
The diverse amplification of data science in nearly all spheres of life – from commerce and transportation to telecommunications and public health – has made us realise how indispensable it actually is.
Big data and data science have been transforming the public health sector, fundamentally changing and improving how even the most basic of procedures such as heart health monitoring is done. And it’s all done by drawing information from often unstructured/raw data and making sense of it.
However, data science in public health is not exactly a completely new concept as data analysis in healthcare has been utilised rather extensively in everything from remote diagnosis and e-medical records to AI-powered medical consulting and prescriptions. But it wasn’t until after the pandemic that data science really started coming into its own.
Through the various applications of data science in public health, it has now made it possible for doctors and healthcare providers to detect the symptoms of a range of diseases, and from a fairly early stage too. Plus, with the advent of specific technologies within data science and public health, doctors can monitor specific health conditions from remote locations. In fact, we have come to a point, where various data and metrics can be collected on patients by having them use a wearable device, which can send important data straight to their doctor’s devices – thus, eliminating the need to schedule clinic visits unless, that is, they are deemed absolutely necessary (such as when surgery is required).
Many years ago, hospitals were simply unable to accommodate beyond a certain number of patients. Furthermore, the tools and technologies were nowhere close to where they are today, making patients' condition worse.
The tables have now turned, so to speak, and public health data science has a governing role to play in this regard. For example, data science and ML (machine learning) applications have helped doctors to stay informed on the health conditions of their respective patients through wearable devices, as we just discussed. If those patients do require an in-person check-up, the healthcare facility management can conveniently dispatch assistants, nurses, or junior doctors to tend to the patient. This can be extremely useful in cases where elderly patients, for example, may not be able to travel freely.
Many hospitals have been quick to capitalise on the public health data science revolution, installing specialised equipment and devices to help patients report health symptoms either from their home or a designated point/kiosk at a healthcare facility nearby. These devices – with data science supplying all the information at the core – can collect data seamlessly from patients, such as their blood pressure, heart rate, body temperature, etc.
Doctors access this patient data through updates and/or notifications on their mobile devices. This helps them diagnose conditions quickly, without ever having to call patients in or scheduling appointments and follow-ups at the clinic. In turn, it also helps nurses or junior doctors to whom the primary doctor may delegate follow-ups to, where patients need to be visited at their premises for treatment.
This is just one example of how public health and data science coincide, helping both healthcare facilities deliver the best care experience possible and patients receive quality care from the comfort of their home.
There are many factors that make data science in public health absolutely indispensable in the present day, with the most important one for healthcare providers and hospitals being the ability to access highly valuable information, all of which, collectively, can help them streamline operations and gain a competitive edge.
The collection of data on patients through the right channels can dramatically help in improving the level of care and additional services healthcare providers can provide to patients. Everyone from doctors and health insurance companies to institutions and other stakeholders depend on the collection of factual data and its accuracy as well as timely analysis – in order to make strategic and well-informed decisions about patient outcomes.
Today, diseases can be predicted much earlier through data science in public health, which means better treatments and chances of recovery. The added advantage is that all of this can be done remotely through devices which are powered by AI (artificial intelligence) and ML (machine learning). Mobile apps and smart devices can be programmed or ‘trained’ to collect patient data like blood pressure, heart rate, body temperature, blood sugar levels, etc., transferring it to doctors in real time, who can devise a treatment plan without delay. This in itself is a competitive advantage that healthcare facilities, clinics, and hospitals can no longer afford to ignore.
As such, there are a number of use cases for public health and data science, whether it’s the discovery of new pharmaceuticals for improved treatments and outcomes or streamlining hospital operations and patient care:
Data science has made significant contributions to the pharma industry in recent years, laying down the groundwork for the development of breakthrough drugs through AI. Processes like mutation profiling and patient metadata are being used to develop new drugs and compounds, which can address the statistical correlation between various attributes. What this means is that AI can predict how effective a new drug will be and how different patients might react to it, or what the general side effects will be, for example.
Predictive models are being used for drug development, helping pharma companies better understand patient requirements and what kind of care they require to make a speedy recovery, while trained algorithms can help companies understand if a specific kind of drug or medicine will be in demand or not.
Data science in public health is also being used to improve the effectiveness of clinical trials by automating specific processes, leading to reduced costs and increased accuracy.
Remember how we discussed that the typical human body generates 2 terabytes of data each day? The advancements in data science in public health have led to the development of wearable devices which allow doctors to collect specific patient data – stress levels, blood glucose
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levels, brain activity, sleep patterns, heart rate, etc. With the aid of ML algorithms and specific data science tools, physicians can quickly detect and also track common health conditions in their patients, like respiratory and cardiac diseases or neurological disorders.
In fact, the latest advances in public health data science can help doctors detect even the smallest changes in their patients' health indicators, making predicting potential disorders easier and more efficient. As part of an IoT (Internet of Things) network, multiple wearable devices and home appliances are now capable of utilising real-time analytics to quickly predict if a patient will likely face any health issue or emergency, based on their present condition or vital stats.
The IoT phenomenon has proven to be nothing short of a blessing in public health data science – the ability for doctors to remotely track patients' vital health statistics and prescribe treatments on the spot, is something that healthcare businesses should not take for granted and capitalise on without second thought.
Cutting-edge wearable devices and sensors can even predict changes in the eye, mouth, skin, and teeth, allowing specialists to administer the appropriate treatment in an efficient way, before a patient’s symptoms get worse.
The data generated from these wearable devices and/or sensors are now a cornerstone of healthcare and public health data analytics, with technologies like ML, AI, Big Data Intelligence and IoT working at the core to supply the required information. There have been many successful outcomes through the utilisation of such data in multiple branches of medicine, including surgery, radiology, geriatrics, neurology, cardiology, and oncology.
Today, chatbots and AI platforms designed by data scientists are helping people get a much better idea of their health when they input specific health information into apps, getting accurate diagnosis as a result. These platforms are even aiding patients with better lifestyle choices and understanding what their health insurance policies cover.
‘Interactive’ healthcare continues to gain momentum with virtual assistants and chatbots who are dedicated to specific tasks – such as one for appointment rescheduling and another one providing people with 24/7 answers to COVID-related queries. This has dramatically eased the call-taking and appoint-scheduling load hospitals and doctors typically face when they must deal with hundreds of queries each day.
Apart from easing doctor burden, the application of data science-driven chatbots and virtual assistants has led to a significant reduction in care costs, reduced wait times, more timely medical advice, improved scalability without compromising the cost of quality of care, and increased patient satisfaction.
The ability to handle a very high volume of inquiries, improve patient outcomes, provide answers in a discrete manner where patients' personal data is always well-guarded – has led to not only cost savings and improved patient experience but also a competitive edge for healthcare providers who are fully utilising chatbots and virtual assistants.
Diagnosis, an integral and crucial part of medical services in all fields of medicine, can be made easier and faster through data science public health applications. The analysis of patients' data can not only facilitate in detecting health issues at an early stage, but also in medical heatmaps related to specific demographic patterns which can be quickly prepared as and when needed.
AI-based techniques like ML and DL (deep learning) in public health data science are being used to detect skin, liver and heart diseases, so that they can be treated at the earliest stage of discovery. In fact, AI can now even be trained on population-specific demographics and environmental factors to better understand the frequency of illness in specific areas or certain patient demographics, as well as detect high-risk behaviours which may lead to health problems.
We are at a point where AI can assist healthcare providers in different kinds of patient care and intelligent health systems – with namely ML and DL for drug discovery, disease diagnosis, and patient risk identification. By drawing data from multiple medical resources, these applications can diagnose diseases almost ‘perfectly’, using AI-based techniques like computer tomography (CT) scan, genomics, mammography, magnetic resonance imaging, ultrasound, etc.
Healthcare professionals use a number of imagine techniques (MRI, X-Ray and CT scan) to better understand and visualise the body’s internal organs and systems. DL-based image recognition technologies in public health data science can help detect even very minute deformities in the scanned images, helping physicians and specialists to come up with more effective treatment strategies.
Common ML algorithms used in healthcare for image analysis include:
Image processing algorithm used for the analysis, enhancement, and denoising of images
Anomaly detection algorithm used for the detection of bone fracture and displacement
Descriptive image recognition algorithm used for the extraction and interpretation of data from images, as well as merging multiple images to help doctors see the bigger picture
Data scientists in public health are also working hard to develop more advanced techniques which will further enhance medical image analysis. For instance, in a publication of Toward Data Science dating back to 2021, the Azure ML platform was utilised to train and optimise a model which helped to detect the presence of common brain tumours like Glioma, Meningioma, and Pituitary.
AI is now being more commonly used to improve image analysis and interpretation in medical imaging. By training specific AI algorithms, specialists can analyse medical images to detect abnormalities or minor changes which may otherwise be very difficult for humans to detect (even using advanced medical equipment). This has led to more accurate and efficient diagnosis as well as treatment of various health conditions.
AI will continue to be used for automating routine tasks in medical imaging, including quality control, data management, and the actual image processing. Through the automation of these tasks, AI will help improve both the accuracy and efficiency of medical imaging, thus, improving the quality of patient care.
Additionally, AI can be used in the management and organisation of massive amounts of medical imaging data, making it much easier for healthcare providers to access and analyse the data on a daily basis. This will definitely help to improve the accuracy and efficiency of both diagnosis and treatment, as healthcare providers will have a far more comprehensive view of patients' medical history and imaging data.
However, it’s worth noting that while AI can help to automate routine tasks in medical imaging and reduce hospital workloads as well as improve patient care quality – the algorithms need to be developed in a responsible and ethical way, focusing strictly on patient privacy and safety.
A predictive analysis model is one where historical data is utilised to seek specific patterns which can be used to generate highly accurate predictions. This data may revolve around anything from a patient’s blood glucose levels to their blood pressure or body temperature.
The way predictive models work in data science is that they correlate and associate each data point with specific symptoms, habits, and diseases. This can reveal insights like what stage the disease is currently in, the extent of damage it has done, and the ideal treatment measures to be taken. In addition, predictive analytics in public healthcare can help in:
Managing chronic diseases
Monitoring and analysing the current demand for pharmaceutical logistics
Predicting future patient health crisis
Delivering faster hospital data documentation
As you might have guessed from the above examples, within the public health data science context, predictive analytics use big data and AI to come up with solutions – aggregating huge volumes of data from EHRs (Electronic Health Records), administrative paperwork, insurance claims, medical imagine, etc. to process it for patterns. This can help doctors and specialists uncover different kinds of patient-related data, such as:
The kind of diseases a certain patient is likely to develop
The chances of success when it comes to the patient responding to different treatments
The chances of a ‘patient no-show’ in successive medical appointments
The chances of a patient returning to the clinic or healthcare facility within 30 days following a discharge
Healthcare providers can, therefore, benefit from predictive analysis in multiple ways, including:
Reduced costs which are typically associated with appointment no-shows and readmission charges
Speeding up of administrative tasks like discharge procedures and submission of insurance claims
Lowering the chances of ransomware-based attacks or other cyberattacks by analysing current transactions and assigning them with unique risk scores
Preparing proactively for upcoming population health trends
Bringing new patients on-board through more personalised campaigns
From the above examples and use cases, we can immediately draw some benefits:
Undeniably, the most vital benefit of public health data science is cutting down human errors in treatment through more accurate prescriptions and predictive analysis to better understand future outcomes.
A substantial amount of patient data, including their medical history, is collected by data scientists, which is stored and used to identify symptoms and illnesses by studying specific patterns as well as trends. All of this can lead to more accurate diagnosis and disease/illness prevention in future.
This has also made treatment options more personalised, the level of care more informed, and the mortality rates lower.
In order to provide patients with quality care and treatment, it is important for physicians and healthcare providers to develop certain skillsets which can help in providing more accurate diagnosis. Through predictive analytics, for example, it is possible to predict if patients are at a higher risk of contracting a certain illness or disease, and how healthcare professionals can best intervene to prevent the often harrowing adverse effects of the said disease/illness.
Developing drugs is a very time-consuming process which requires intensive research, clinical trialling, and approval-seeking from the appropriate body. However, through public health data science, these efforts can be sped up – by using past medical data, lab testing results, case study reports, and the effects drugs had in clinical trials, all of which are fed into ML algorithms to quickly predict whether the drug will produce positive effects on the human body or not.
EHRs can be used by medical data scientists to quickly identify the health patterns of patients, thus, preventing unnecessary admission or hospital-based treatments, which means reduced operating costs for healthcare facilities.
We are witnessing some very remarkable outcomes in the 21^st^ century where heavy use of public health data science is helping to optimise surgeries, patient diagnosis and care, and internal facility operations as well as patient outcomes and recovery procedures.
Other than the technological advancements made in the field of public health and private healthcare as well as the increased digitisation of lifestyles across nearly all demographics, data science will undoubtedly help hospitals and healthcare providers work more efficiently by reducing operational overheads and cost of treatments, ultimately, making quality medical care and amenities available to all.
The above data science public health applications are just some of the examples we could think of to show you how in the near future, we’ll be seeing a lot of big data, analytics, AI, and ML in the public healthcare sector – which will ultimately help diagnosing and treating a broad range of illnesses more effective, while also helping healthcare providers operate in a more cost-effective way.
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public health data science
data science public health
Meta description: Data science in public health: Common applications to know
How is data science in public health being used? Can public health and data science complement each other? This, along with common applications and more.
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Above: video of the AICamp meetup in London on 10 December 2024. Harmony starts at 40:00 - the first talk is by Connor Leahy of Conjecture
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