Strengths of Employing Data Science in Healthcare

Alexander Gerasimov -
Illustration: © IoT For All

Data science employing big data for healthcare needs and the extraction of valuable business insights greatly transformed the medical industry and brought revolutionizing results in care efficiency and personalization. 

According to Global Market Insights, the healthcare analytics market size is expected to grow by 12.6% by 2025, and the prescriptive analysis sector is the one that will witness the highest level of expansion with 15.8% against 13.2% in the clinic end-use segment.

Access to medical databases leading to the deployment of data makes it possible to shift from medical treatment that takes up a lion’s share of healthcare budgets, and rather focus on identifying the preventable illnesses (for instance, two leading avoidable deaths conditions are ischaemic heart diseases and lung cancer) and primary and secondary prevention.    

Big Data Benefits

Medical data is a powerful resource for deriving valuable insights and reducing data waste. In the context of new reality associated with an overload of healthcare and pandemic challenges, big data can assist healthcare providers in detecting health-related patterns turning vast data into actionable information vital in medicine and medical industries.

Aside from patients getting whose experience of healthcare service can be enhanced as a result of applying data science, the stakeholders interested in the implementation of big data in the healthcare sector include healthcare providers, the health tech industry, pharmaceuticals, and health insurance agencies.

Among multiple benefits of employing big data in healthcare, the following ones come on top: 

  • Implementation of data science in healthcare allows to create comprehensive patient profiles.
  • It provides instant identification of patterns in treatment outcomes
  • It enhances patient satisfaction
  • It facilitates hospital administrative workflows 
  • It optimizes medical procedures by increasing care efficiency
  • It enables the medical industry to be more cost-effective. 

Overall, data analysis in healthcare ensures a highly personalized approach to customers and processing of an individual patient model that can map out their health history and health course trajectory digitally, which implies multiple sharing options, wide diagnosis capabilities and deeper engaging patients in medical decision making. 

Furthermore, the data analysis helps to improve the productivity of the healthcare sector as it enables the medical industry to maintain the high quality of the service with fast processing of a large amount of existing (and prospective) medical data at a reduced cost. 

Although the application of healthcare analytics is somewhat limited in Europe, a pandemic caused by COVID-19 forced authorities to reconsider the previously imposed restrictions and give the green light to healthcare( in particular, predictive and prescriptive) analytics initiatives.

Big Data Challenges

Due to the sensitivity of health data, its fragmented nature, the enormity and complexity of databases, and the special importance of privacy-preserving technologies, data science in healthcare can face certain challenges. 

In particular, challenges of processing and analyzing big data in healthcare that might restrain the market growth mostly pertain to: 

  • the shortage of  IT professionals with relevant expertise
  • data integrity issues 
  • ensuring data safety. 

Besides, complexities of regulations and lack of unified procedures in the healthcare industry can create barriers to wider application of data analytics by medical providers and hinder the growth of the health data analytics market.  

Data Science Applications

Data science in healthcare ensures a full overview of the patient’s profile in real-time as it lets process clinical information including patient demographics, diagnosis, medication, procedure, lab results, and additional clinical notes.  

The large amounts of medical data that became available in healthcare organizations resulted in opening opportunities for successful completion of multiple data science projects: among illustrative applications, the most outstanding belong to practical clinical environments. 

A number of pioneering organizations (Cerner Corporation, International Business Machines Corporation, MedeAnalytics, Oracle Corporation, etc.) generate use cases in and outside the clinical environment to show the potential of further exploration of data science in healthcare and its positive transformation. 

They made a breakthrough in the market of wearables (they covered the various domain areas including fitness, exercise, movement, physical activity, step count, walking, running, swimming, energy expenditure, etc.), and diagnostic tools demanding implementation of advanced analytical models. 

In general, the incomplete list of data science applications includes the following areas: 

Medical Imaging

In this particular scenario, computers demonstrate self-learning abilities to interpret MRIs, X-rays, mammographies to recognize patterns in the data and find tumors, or any organ anomalies. 

Genomics

In this case, data-processing tools through analysis and interpretation help to come to an understanding of data from next-generation sequencing experiments.

New Drug Launch

Pharmaceutical companies use data science to make financial predictions and the potential market impact of a new drug by analyzing the operational pipelines from manufacturing agents to end-use consumers.

Predictive Analytics Purpose

By extracting deliverables from data, medical industries use it to predict trends and behavior patterns to enhance healthcare customer experience and calculate probabilities of medical outcomes based on the statistical approach.

Monitoring Patient Health

By storing digital health-related information of the patients, healthcare providers can improve the productivity of healthcare delivery systems. Besides, data analysis is used to monitor health parameters including blood pressure, body temperature, and heart rate in real-time.

Tracking Health Conditions

Data science can provide ongoing accurate tracking of health conditions and mark potential cases that a patient is prone to. For instance, data science proved to be an invaluable asset when it comes to assisting individuals with diabetes in keeping track of the meals, physical activity zones, and blood glucose levels. 

Providing Virtual Assistance

With the comprehensive platforms available due to data science, patients are provided with the means of identifying the disease by entering the respective symptoms in the application search bar. The virtual assistant will immediately identify the condition and offer to choose the possible health solutions.

Data Science Access 

Access to big data and data science in healthcare made a positive impact on the practice of medicine with widening capability of medical professionals to apply data-driven decision making, take a personalized approach while treating patients and instantly checking real-time data against patients’ profiles for delivering high-quality healthcare. 

It allows us to be confident in forecasting the bright future of data science and further development of tools for comprehensive analysis in healthcare linked in the expansion of the market of data science applications.  

In addition to providing new levels of data completeness and interoperability, they can successfully address, among various issues, the problems with disease prevention, symptoms, monitoring health conditions, dosage calculations, and pharmaceuticals. 

Author
Alexander Gerasimov - CTO @ CodeIT, CodeIT

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Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All. If you're interested in contributing to IoT For All, cli...
Guest writers are IoT experts and enthusiasts interested in sharing their insights with the IoT industry through IoT For All. If you're interested in contributing to IoT For All, cli...