Key benefits and challenges of using Big Data in healthcare

What Is Big Data In Healthcare?

Big Data refers to large amounts of data gathered, analyzed and processed to discover dependencies, trends and considerable conclusions. It supports better decision-making, improvement of the services, customizing the products, reducing costs and reaching many other goals in business, social and scientific fields. Within the healthcare sector, Big Data covers every each of these areas as it is widely leveraged by both - public and private sectors. The solution is powerful and able to revolutionise the whole business strategies, helping to invent new medicines, explore the DNA and in the trying times of COVID-10 pandemic - to track the virus spreading. 

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)

On the other hand, with the use of modern technologies and tools and support of qualified specialists, it can be utilized also by healthcare startups, smaller companies and organizations. Harnessing the potential of Big Data, machine learning and data visualization leads to designing the new digital products, expediting processes and adjusting the services to a very special and personal needs of users.

Looking for interesting Big Data and Machine Learning implementations? Check how we harness them to build:

- Logo creator enabling to make branding regardless of skill level or background in design
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In spite of all challenges and requirements related to the proper implementation of the solution, it briskly gaining popularity in the medical industry. The foreseen global Big Data analytics in the healthcare market size is valued at $67.82 billion by 2025, growing at a CAGR of 19.1% from 2018 to 2025. So what exactly convince companies to introduce Big Data in the healthcare industry?

Benefits of using Big Data in healthcare solutions 

More accurate diagnoses and treatment

Access to the vast amount of data and the ability to draw conclusions based on them are the most up-and-coming method of supporting the diagnosing process. The physicians don’t have to rely only on their own experience and expertise anymore but reach to the massive resources of electronic patient records (EPRs), images, sounds, prescriptions, test results, and so forth. This translates to more effectual therapies, detecting diseases on their early-stage, choosing the medicines and the doses more precisely. As a result, deploying Big Data reduces the risk of errors causing longer and more burdensome treatment and even deaths of patients. Data-driven decisions save costs and time but most of all - humans’ health.

Decreased overall healthcare costs 

Reaching even 16.9% of GDP (USA) the costs are a tremendous part of all total spendings in both government and private sides. The OECD average in 2018 was estimated at 3 994 dollars per capita per year but this measure is highly diversified across the countries spreading from only $209 in India to more than $10k in the USA. 

Health spendings in OECD countries, source: data.oecd.org 

Digital transformation is one of the most promising ways of cutting expenses and improving the efficiency of healthcare services. Leveraging Big Data may help to attain these goals in multiple ways. In administrative area it expedites the processes and management, in disease treatment, it supports fast diagnosing and the right choice of medicines and in prophylaxis, it helps to monitor the health indicators more accurately and react promptly in case of abnormalities.  

Better user experience and personalization

Improving the quality of services and products is unreachable without a deep understanding of users’ behaviours and habits. That refers to health customs in particular as the way they act has an immense influence on their physical and mental condition, effectiveness of treatment and also simply the usage of apps and other software. Rich data sources provide insights from both - the everyday routine of users (e.g. gathered through the IoT devices) and the utilization of particular solutions. Studying them profoundly equips with invaluable knowledge and hints on making the health services and products more friendly, personalized and better meeting the real needs.        

Improving the management and administration 

Although healthcare centres and hospitals perform in a quite specific environment, they can and should analyze their operating costs like common companies in order to boost the efficiency of budget expenditures. Big Data enriching their compilation of information with historical figures (and sometimes even real-time ones) can significantly facilitate planning and management. It helps to prepare more accurate estimations of supplies, predict an increased demand for doctor's appointments, redesign workflows or streamline making up the duty roster. With the already mentioned researches of user behaviours, Big Data can be exploited to lift the results of marketing and sales efforts and other business activities like customer service and helpdesk. 

Big Data or maybe Data Warehouse? Check which solution fits your need better. 

Making healthcare services more available 

Big Data is also gaining ground in one of the major areas of health tech - telemedicine. The impressive growth of this sector expected to reach 175 billion U.S. dollars by 2026 is driven by the extensive deployment of remote consultation as well as emerging technologies like IoT, machine learning and AI.

The growth of telemedicine market 

Conducting the examination by a doctor being miles away from the patient might be extremely challenging especially in case of more grievous illnesses and injuries. Health information from personal wearables or smartphone apps and historical medical data of particular disorders and examinations can significantly contribute to making a correct remote diagnosis. This is exactly how StethoMe - a wireless, electronic stethoscope fuelled with AI-based system - supports identify lung and heart abnormalities. It delivers the recording of the auscultation together with AI analysis based on 1 015 866 sound tags and 38 530 detailed medical descriptions. 

Meet more companies switching medical industry to the remote mode!

The ability to meet the doctor without leaving the house makes healthcare services more accessible but also reduces the costs of the visit. The patients don’t resign from the consultation downplaying the symptoms (which often leads to belated initiation of treatment), but on the other hand, they limit the appointments in the doctor's surgery caused by the ordinary flu. 

Enhancing the prophylaxis with health monitoring solutions 

The increasing number and advancement of apps processing data provided by sensors and users fuels health monitoring development. They don’t only serve as the assistants in making our lives more comfortable but also reinforce preventative medicine which is more cost-effective as well as hard to implement. They analyze personal health data and present it to the user in a simple and aesthetic manner, deliver conclusions and advice and warn of worrying symptoms.

The health data visualised in a mobile app, source: Dribbble

Sharing the data from health monitoring solutions with doctors and med institutions may help in preparing more precise treatment in the future. 

Challenges in leveraging Big Data in Healthcare 

Overwhelming volume, velocity and diversity of data

Today, the data concerning health are widely gathered within the medical systems as well as millions of private IoT devices and mobile applications. Aggregating them from many different organizations and objects is the first task. If it’s accomplished there appears the challenge of dealing with this colossal size of records. The total volume of healthcare data is estimated to reach 2,314 exabytes in 2020 (while just 7 years ago there were over 15-times less of them - 153 exabytes). Although it does feed the machine learning algorithms, swelling databases growths the cost of collecting and processing information. The number of records is not the only problem but also the pace of generating them which can cause bottlenecks and overcharges. 

A large quantity of data sources induces the mixing of different types of information: audio, video, image and text formats, official and unverified. The point is to structurize them and combine the conclusions of analysing the different categories. 

Legal restrictions and data security

Health data are probably the most sensitive ones and thus imposes the strict requirements in terms of collecting and exploiting them. There are special principles dedicated to protecting them in overall regulations like GDPR and also the whole separate documents like HIPAA which point the terms and conditions of processing data in details.  

Learn how to make your app GDPR and HIPAA compliant! 

The goal of these restrictions is to keep the medical information secure in all dimensions like defined within HIPAA - integrity, confidentiality and accessibility. This approach covers all the bases in the healthcare data protection including the ones being in rest and in transit, processed by a particular organization and its suppliers, safeguarding the access to them and counteracting unintended changes. The fines for not conforming with legislation might make significant harm to the budget as they reach up to 1.5 million USD per year per violation in case of HIPAA settlements. 

Data engineering talent shortage   

The demanding field of Big Data requires a top-notch combination of mathematic and programming skills. No wonder Data Engineer was the fastest-growing job role in 2019 according to DICE’s Report.

The increasing demand for Data Engineers, source: Tech Job Report

The research also shows that it takes 46 days on average to find the expert in this field while the salary on this position reaches $100 000 per year. There are also a lot of hidden costs of hiring in-house engineers and the constant risk of quitting the job due to strict and constant competitiveness.  

Do you need the Big Data in your healthcare solution? 

The decision of implementing Big Data needs to be based on the project’s or organization’s specifics and needs. Using it doesn’t always mean deploying the most advanced tools powering the algorithms investigating genotypes. Still, a huge part of medical software and apps requires some sophisticated technologies starting from machine learning models to an interactive data visualizations. With expert advice, healthcare companies can find the most cost and time-effective way of utilizing their data to take the full advantage of gathered information.   

Looking for a reliable tech partner to support leveraging Big Data in your healthcare app? Check our expertise in Machine Learning!