Data Science in Healthcare: Advantages and Applications — NIX United

12 min readAug 3, 2023

The healthcare industry is one of the most complicated sectors to manage and optimize. While many industries are mainly focused on generating profit, medicine needs the patient at the center. Physicians are expected to do their best to diagnose and treat their patients, no matter the circumstances. Unfortunately, reality is not as simple. Healthcare institutions often struggle with staffing shortages, budget cuts, and outdated equipment. Data science in healthcare is a promising field that can change the system and benefit hospitals, medical personnel, and patients.

As a discipline that includes various technologies and techniques, data science can contribute to the development of new medications, prevention of diseases, diagnostics, and much more. Utilizing Big Data, the Internet of Things, machine learning, artificial intelligence consulting, etc., allows data scientists to revolutionize the entire sector. In this article, we will examine various applications and use cases of data science in healthcare as well as investigate its benefits.

Applications of data science in healthcare include consolidating all the data about the human body to generate and derive important observations. Considering the human body generates two terabytes of data on a daily basis, from brain activity to muscle performance, scientists have a lot of information to collect and process. Metrics like heart rate, brain activity, temperature, and more, can be analyzed much quicker using data science methods. On top of this, technologies like the Internet of Things (IoT) allow doctors to monitor patient’s health remotely.

The Role of a Data Scientist in Healthcare

Data science is a discipline with unlimited potential when it comes to enhancing the medical sphere. Instead of letting all the accumulated patient data stay idle, hospitals can make use of it and advance their diagnostic and preventative capabilities. Besides this, historic data can aid in optimizing inventory management and supply chain processes, making medical institutions more efficient.

The central role of a data scientist in healthcare is to deal with large volumes of data and draw meaningful conclusions based on it. Data management includes compressing, structuring, and converting data.

Here is the list of the duties that a healthcare data scientist usually performs:

Defining the goals of the project as well as tools and software required
Working with large amounts of structured and unstructured data aiming to organize patient data files
Cleaning data to meet the organization’s requirements and objectives
Performing data analytics for the company’s internal systems and applications
Coordinating with developers to create various models and simulations and register the results

The main goal is to organize the information in a way that makes it accessible and understandable for non-technical employees. The next step involves applying analytical skills to discern patterns that can aid in diagnostic procedures. A data scientist in healthcare sifts through massive sets of data and runs various models and tests to identify any correlations and connections. Later, they provide their insights in reports and dashboards for physicians to make data-driven decisions.

Benefits of Data Science in Healthcare

Data science in healthcare provides numerous benefits for healthcare and caregiving companies, physicians, and patients, increasing the overall level of public health. Using various methods and techniques, data scientists can help in developing groundbreaking medications, predicting epidemics and outbreaks long before they pose a threat, and improving diagnostic capabilities across hospitals. In this part, we will cover the primary advantages of adopting data science in the medical sector.

Reduces Failure in Treatments

One of the most imperative data science benefits is minimizing errors and misdiagnoses. Various technologies are used to detect drug resistance, allergic reactions, and other conditions. By increasing symptom awareness among the population, physicians can reduce their workload. Knowing the basic symptoms of common diseases allows patients to take charge of their health and seek medical attention before the illness gets worse.

Data science applications in healthcare also include the identification of complex conditions that are undetectable by the human eye. For example, various IoT tools can be used for monitoring and analyzing brain waves and sleep patterns that help to recognize epilepsy. These findings can lead to the discovery of new drugs and treatments.

Facilitates Drug Development

Drug research and development requires massive time and money investments that often don’t pan out. However, using existing historical data and studies allows a healthcare data scientist to accelerate the research. The implementation of machine learning algorithms enables the prediction of drug performance and side effects. Mathematical modeling and simulations can also facilitate a perfect cost-effective environment for testing medicines. Instead of conducting tests in real life, scientists can run simulations to see the outcomes of various active ingredients.

Eliminates Manual Errors

Whenever manual labor is part of a process, corporations are bound to make mistakes. People get tired, overwhelmed, distracted, and burnt out, which will inevitably lead to an error. In the medical setting, even a small oversight can result in severe consequences. Employing automation tools to perform repetitive and routine tasks will reduce the number of mistakes and allow workers to focus on their primary duties.

Minimizes Healthcare Expenses

Data science in healthcare can also help hospitals and clinics to reduce operational costs. Firstly, technology can replace some of the manual labor thus eliminating overhead expenses. Tools like appointment scheduling software and medication reminders can potentially reduce the overall workload and save you money.

Another cost-effective benefit is the improvement of diagnostic capabilities. Making more precise and timely diagnoses prevents unnecessary hospitalization and patient complaints. On top of that, doctors can diagnose faster, thus saving money while treating more patients.

Finally, tools for inventory management help medical institutions prevent overstocking and waste. Having an application that automatically monitors the quantity and condition of medical equipment and drugs reduces the chances of misplacing an important item. Moreover, such tools will notify staff when a drug is about to expire or needs to be restocked.

Predicts Epidemics

In the post-COVID world, medical professionals understand the importance of timely predictions of outbreaks and epidemics. Data science in healthcare is capable of analyzing vast amounts of information to learn patterns of disease occurrence. Knowing these patterns helps experts mitigate potential outbreaks and develop various scenarios. The same methods can be used to identify psychological diseases like depression and anxiety. A data scientist in healthcare can recognize how the symptoms grow and change which helps physicians not to miss a serious development and start treatment early.

Offers Personalized Treatments

The large volumes of health-related data allow scientists to develop personalized recommendations based on unique genetic makeup. Oftentimes, a certain medication works perfectly on one patient and fails on another. A healthcare data scientist can identify the specific differences and predict which drug is most likely to help a patient. These findings aid in the development of specialized devices and software to cover a patient’s unique needs.

Application and Use Cases of Data Science in Healthcare

Data science in healthcare offers numerous applications and use cases that can improve experiences for both medical staff and patients. From assisting patients via apps to discovering new drugs and treatments, the latest advancements in technology carry great benefits for the entire healthcare industry.

Discovering Drugs

Drug development is a complicated industry, involving several stakeholders, large investments, and rigorous testing. On average, it takes pharmaceutical companies up to twelve years to develop and officially launch a new drug on the market. However, by applying historical data, corporations can speedrun some tests using a specific mathematical model and simulation. Instead of conducting experiments in the lab, technicians can run tests using technology thus saving time and money. Moreover, they can improve the safety of clinical trials to minimize any potential harm to the participants.

Virtual Assistants

Data science in healthcare is also responsible for creating various virtual assistant applications. They can be used for a multitude of reasons, including self-diagnostics, treatment plans, medication scheduling, etc. Although such solutions do not eliminate the need for a medical staff, they certainly can take on some of the workload. For example, a patient will get notified whenever they need to take medication or arrange a new appointment. Furthermore, the app can aid patients with mental disorders like depression or neurodegenerative diseases in completing their daily tasks.

Based on IoT technology, wearables are pieces of equipment that measure patients’ vitals and transmit the data to clinicians. These tools are geared with sensors that monitor blood pressure, temperature, heart rate, or other vitals and send the information to the cloud. Doctors can access the cloud and oversee the health data over the span of days, weeks, or months for a more comprehensive diagnosis. Moreover, physicians can be alerted whenever a certain metric goes outside the norm to schedule an appointment or change the treatment plan.

Disease Tracking

A use case that emerged amidst the recent pandemic-the ability to track outbreaks, especially in the field of infectious diseases-can be extremely helpful. Data science allows physicians and virologists to understand how pathogens spread in real-time. Not only can this facilitate forecasting, but also help prevent viruses from spreading further. This data can help specialists trace the outbreak back to patient zero and isolate infected people. These measures allow for data-driven resource allocation and timely treatments.

Diagnostics

Among other data science applications in healthcare is the ability to issue a correct and timely diagnosis. Diagnostics is a highly complicated discipline that requires extensive knowledge and experience as well as advanced medical equipment. One in 18 people in the US emergency rooms are misdiagnosed, according to the US Department of Health and Human Services’ Agency for Healthcare Research and Quality. This leads to potentially more serious diseases, dissatisfied patients, and subsequently even more workload for the medical personnel.

Data science in healthcare allows physicians to access patients’ health data, see the change over time, and tweak the treatment plan if something goes wrong. Utilizing big data analytics allows medical professionals to take advantage of historical information and get valuable insights.

Identifying Patient Risks

Patient risks derive from a plethora of various factors, both physiological and socioeconomic. Using data science in healthcare, specialists can determine the main risks for each individual, calculate their overall health condition, and develop a set of personalized guidelines. For example, if a person is predisposed to a certain type of cancer, a doctor would recommend an annual screening to catch it in time.

Medical Imaging

Imaging techniques like MRI, CT, and X-ray are used on a daily basis in every hospital. These images help doctors see what is inside the patient’s body and identify the root cause of their pain or other symptoms. Traditionally, physicians study these images to identify a defect or irregularity that allows them to make a diagnosis. However, data scientists in healthcare have employed deep learning technologies to enable easier analysis. Such programs detect even microscopic abnormalities through image segmentation.

For example, deep learning algorithms have already shown impressive results in detecting 26 skin conditions on par with certified dermatologists. Using three main anomaly detection, image processing, and descriptive image recognition algorithms, a healthcare data scientist can extract valuable data from the image, interpret it, and suggest a diagnosis.

Genomics is a discipline that studies the sequencing of genomes that carry all the genetic information of the human. They consist of DNA sequences and are integral parts of future advancements in the medical field. Traditionally, DNA sequencing and analyzing used to be a tedious and costly procedure. Luckily, the use of technology can significantly accelerate and streamline the development of genomics.

Using data science in healthcare, researchers can derive valuable insights that genes carry in a shorter time and at lower costs. By analyzing genomic strands, healthcare data scientists can find irregularities that connect the genetic code to the person’s health status. In other words, experts can discover correlations between genes and various diseases.

The developments in genomics over the recent years have contributed to the emergence of a new field of study called bioinformatics. Using tools for processing and analyzing genetic data, scientists can create and test new drugs and shine more light on how our genes determine our health. For example, Galaxy is a biomedical research application that helps experts conduct diverse operations on genomes.

Predicting Diseases

Predictive analytics utilizes data science in healthcare to forecast the patient’s health condition. By analyzing existing information pertaining to their previous illnesses, test results, and current vitals, specialists can identify patterns. This method allows doctors to identify the symptoms of a particular disease, its developmental stage, the damage done, and more. All of these findings help paint a clear and comprehensive picture of one’s health. Having such extensive information available, clinicians can come up with the appropriate treatment plan that fits this particular patient.

Vaccine Development

Data science has played a pivotal role in the development of the COVID-19 vaccine. Using data analytics, healthcare data scientists were able to accelerate both the creation and manufacturing of the vaccine. Studying historical data helps specialists identify symptoms and see how the disease affects people. Using these insights, they can develop a vaccine to combat the virus in a much shorter time and at a lower cost.

Digital Security

Data protection plays a major role in any technological breakthrough. Data science in healthcare can also contribute to improving data safety and ensuring confidentiality across medical institutions. From tracking suspicious activity and staying on top of the latest cybersecurity trends, healthcare data scientists increase the level of data protection.

Many healthcare providers still use handwritten health records instead of electronic health records (EHR), either from a lack of resources or out of habit. The issue is that handwritten files often get misplaced or lost. At the end of the day, they still need to make their way inside the system. Natural Language Processing (NLP) can be used to streamline the data transfer. This technology can process unstructured data, take into account grammar and syntax, and identify the meaning of the information. Using NLP-powered software, hospitals can swiftly transfer physical patient files into their computerized systems without spending time on manual typing. This not only saves their valuable time but also reduces the amount of errors.

Biometrics

Biometrics can be applied for a quick and flawless identification process. As the level of confidentiality is extremely high, it’s important not to disclose personal information to third parties. Biometrics software scans patients’ faces and other body parts to match them with the available information. This technology can significantly increase the level of data security as well as accelerate patient check-in.

The Future of Data Science in Healthcare

Data science has a prosperous future in the healthcare industry. The diverse technologies and techniques that we covered in this article can improve patient satisfaction, drive medical research, and make positive systematic change. Applications like genomics, wearables, imaging, and others can contribute to making the system more accessible, cost-effective, and helpful. By enabling early diagnostics and utilizing genomic research, doctors can start treating their patients before the disease progresses.

Data science applications can also assist in post-release care, giving patients autonomy over their health state. At the same time, doctors no longer need to send reminders and worry about patients’ aftercare. Remote monitoring tools are capable of alerting users about medications and new appointments. In essence, both patients and physicians can take advantage of such data science solutions and delegate some of their tasks to them.

While the benefits of data science software are clear, getting all the stakeholders on board can be quite challenging. For this reason, it’s vital to communicate how this technology can improve and optimize their lives. Only if you invest time into breaking down how medical workers can benefit from data science applications can you expect full participation from all stakeholders.

Conclusion

Besides aiding in diagnostics and treatment, data science can optimize hospital management and operations. Data-driven insights can facilitate better resource allocation, reduce manual errors, and impact population health management. All in all, data science in healthcare can enhance the efficiency and accuracy of the services and enable more time for patient care.

If you are intrigued by the promises of data science in the medical field, share your ideas with the experts from NIX. Our team of professionals with decades of experience will help you realize your concept and make your organization more efficient and patient-centric. Get in touch with us to discuss your needs and wants and bring your ideas to life.

Statistical Analysis System is used to extract data from different sources and visualize it
Apache Hadoop is used for processing data across various channels
Tableau is used for data visualization
BigML and Rapid Miner are used to generate machine learning algorithms
PowerBI is used to create insightful reports and dashboards on healthcare data
Trifacta is used to clean unstructured data and prepare it for processing.