Governments can improve medical services for their citizens by leveraging big data in healthcare using tools such as AI, IoT and computer vision.
Every visit you make to a healthcare center creates several data generation opportunities. Something as routine as a standard health check-up involves, amongst others, the updates made to your patient profile in the visitation records, your blood pressure readings, the details related to your blood sugar, platelet count, cholesterol, body fat, and so on. This is the data generated only by a solitary patient making a single clinic visit. If we consider the above scenario for the population of a smart city, what we have is an enormous amount of diverse medical information that evolves with time. In the COVID age, telehealth consultation, diagnosis and medical support have gone mainstream. The convenience of undergoing a check-up remotely with the help of digital tools simply augments the body of data mentioned above. Ultimately, what we have in a smart city is a massive blob of healthcare data that keeps growing and diversifying relentlessly. Unfortunately, public healthcare does not use big data to its fullest, an opinion that resonates with a majority of health experts. The classification and analysis of big data in healthcare are necessary if governments are to improve the quality and quantity of healthcare services provided to their citizens. Leveraging big data in healthcare for optimizing public health involves the capture and use of information generated from patient visits to hospitals, clinics, medical stores, and other sources. Tools such as AI, IoT, and computer vision are useful for data identification, capturing, and analysis in smart cities. So, public bodies need to use such tools to harness the true power of big data in healthcare. Naturally, that process comes with its own challenges, such as the need to clean the captured data, cybersecurity requirements to prevent data breaches, and issues related to data storage and retrieval. However, the incentive of safeguarding public health is enough for government bodies to take the necessary measures to overcome the challenges.
Applications of Big Data in Healthcare
Unlike the private sector, which usually limits its priorities to raking in profits year on year, governments are heavily accountable for public welfare. Apart from certain exceptions, the public sector is largely held responsible for any major healthcare issue faced by the masses. Take the ongoing pandemic, for example, where several governments around the world are facing the heat for managing the COVID outbreaks in their respective countries poorly. To fulfill their healthcare responsibilities, governments need to make the most of the data gathered through healthcare infrastructure. Here are a few areas in which the correct use of big data in healthcare can optimize public wellbeing:
Subsidizing Medication Prices Using Billing Information
The body of big data in healthcare includes the purchase records maintained in drug stores and hospitals. The purchase records of certain drugs or medical equipment paint an accurate picture of how much demand there is for specific items in certain regions. As we know, demand may or may not be consistent across different zones in a smart city. Public health authorities can use IoT-based data receptors, computer vision cameras, digitized purchase records in medical stores and other tools to capture this data from different zones to keep retrieving this data in real-time on a day-to-day basis.
With assistance from AI-powered data analytic tools, health officials can determine the demand trends of specific drugs in every smart city zone. Using this processed information, as well as the insights and predictive analysis provided by AI tools and applications, public health agencies can direct supplies of such medicines and equipment to areas where the demand for them is highest. More importantly, the local authorities can use dynamic pricing strategies to subsidize medicine rates for such drugs in high-demand regions.
As we know, certain medical conditions affect people from specific races or regions more adversely than others. For example, Hemochromatosis, a metabolic disorder, is most commonly found in people of Irish descent. Similarly, more African American people suffer from Sickle-cell disease and Myeloma than people from other races. AI enables public health authorities to deduce such trends promptly from the billing information as compared to standard analytic tools and methods. Accordingly, public bodies can lower the prices and raise the supply of the medicines needed to combat such health conditions for people or communities that need them the most.
Using AI-Based Cybersecurity Tools to Protect Health Records
According to a study, healthcare-based data breaches increased three-fold in 2021 compared to the previous years. Additionally, ransomware attacks in the US healthcare sector accounted for losses of almost US$21 billion in 2020. To prevent such attacks in the digitized public health sector, a combination of big data and AI can be used by public health agencies. AI may have a few problem areas when it comes to healthcare, but it is still the most useful resource to prevent data security breaches and other types of cyberattacks in digitized healthcare. Public healthcare agencies can get the most out of big data with AI-powered data analytic tools. Such tools are generally employed for greater speed and clarity in threat detection in a digitized healthcare data network.
As any cybersecurity expert would tell you, regular patch management and updates are useful to keep a lot of cyber threats at bay. However, stronger attacks need to be proactively dealt with before cybercriminals can remotely get into public healthcare systems. Such malware attacks can cause grave damage to healthcare IT infrastructure at a much quicker pace than the response generated by cybersecurity experts in public health centers. Many organizations are increasingly depending on AI to autonomously take cybersecurity-related decisions by studying the attack patterns of different types of network threats. An immediate response is useful for preventing the spread of a cyberattack to several devices.
AI cybersecurity tools rely on exhaustive machine training data to understand the different types of attacks and how to nullify them. Such tools can then go on to differentiate ‘abnormal’ data flows from regular ones in a network. Differentiation is usually the first step of AI cybersecurity. AI can identify even the most sophisticated threats created by cybercriminals. Even if they get into your network, they can be stopped from causing further network damage. After AI detects an attack, it can orchestrate the other data protection devices and applications to collectively take action to stop it from advancing. The importance of big data and AI in cybersecurity is especially pronounced in public healthcare during the ongoing pandemic, where overburdened hospitals need all the data available to treat patients.
Speeding Up Medical Research
Clinical trials are a significant part of medical research. The inferences derived from such trials enable pharmaceutical companies in the public sector pharmaceutical companies to develop new medicines, machines and therapies. Today, AI has advanced to the point where healthcare scientists and researchers can use it to speed up the process of performing such trials. As we know, when COVID-19 first broke out around the world, there was a ton of data generated from various sources about the virus itself, its transmission, possible mutations and other details. Big data and AI played a key role in harnessing all that information, which came in handy in the development of vaccines in record time.
Apart from speeding up clinical trials, big data, computer vision and AI form a valuable combination to streamline cancer diagnosis and treatment, as well as genomic diagnostics. Generally, private organizations spend more on implementing AI to leverage big data analytics in healthcare functions. Given the huge number of benefits and applications of computer vision, AI and Natural Language Processing (NLP) in private healthcare, governing bodies in smart cities can also deploy these tools to exploit big data in healthcare to the fullest in smart cities.
Apart from these applications, big data can also be used by public health insurance providers to calculate the insurance premium amounts for insured individuals. For calculating the premium, large amounts of data regarding factors such as patient eligibility, usage of strong medications and past health records are studied before determining how much a policyholder will have to pay in periodic installments. While public health has always been a priority for governments everywhere, the emergence of COVID-19 has shown us just how much ground there is still to cover. One of the grim realizations for public health authorities today is that a huge chunk of deaths caused by the virus could have been avoided if the pandemic had been handled more competently. Still, governments everywhere can use the experience of the past 18 months as a valuable reference point to include digitization and big data analytics more extensively to safeguard public health. Although AI and big data have played their part in developing COVID-19 vaccines, public healthcare can be greatly improved in smart cities by involving those two more in the sector.