Governments can improve medical services for their citizens by harnessing big data in healthcare using tools like AI, IoT, and computer vision.

Each visit you make to a health center creates several opportunities for data generation. Something as routine as a standard checkup involves, among other things, updates to your patient profile in visiting records, blood pressure readings, details related to your blood sugar, platelet count, cholesterol, body fat, etc. to. This is data generated only by a single patient making a single clinic visit. If we consider the above scenario for the population of a smart city, we have a huge amount of diverse medical information that changes over time. In the era of COVID, telehealth consultation, diagnosis and medical support have become commonplace. The convenience of undergoing remote control using digital tools only increases the body of data mentioned above. Ultimately, what we have in a smart city is a massive body of health data that keeps growing and diversifying. Unfortunately, public health care does not make full use of big data, a view that resonates with the majority of health experts. Classification and analysis of big data in health care is necessary if governments are to improve the quality and quantity of health services provided to their citizens. Harnessing big data in healthcare to optimize public health involves capturing and using information generated by patient visits to hospitals, clinics, medical stores, and other sources. Tools like AI, IoT, and computer vision are useful for identifying, capturing, and analyzing data in smart cities. So, public bodies need to use such tools to harness the real power of big data in healthcare. Naturally, this process comes with its own challenges, such as the need to clean up captured data, cybersecurity requirements to prevent data breaches, and issues with data storage and retrieval. However, the incentive to safeguard public health is sufficient for government agencies to take the necessary steps to overcome the challenges.

Applications of Big Data in the healthcare sector

Unlike the private sector, which generally limits its priorities to reaping profits from year to year, governments are heavily responsible for public welfare. With some exceptions, the public sector is largely held responsible for any major health problems that the masses face. Take the ongoing pandemic, for example, where several governments around the world are facing the heat to mismanage COVID outbreaks in their respective countries. To fulfill their health care responsibilities, governments must make the most of the data collected through health infrastructure. Here are some areas where the correct use of big data in health can optimize public welfare:

Subsidize drug prices using billing information

The big data set in healthcare includes purchase records kept in pharmacies and hospitals. Purchase records for certain drugs or medical equipment paint an accurate picture of the demand for specific items in certain regions. As we know, demand may or may not be consistent across different areas of a smart city. Public health authorities can use IoT-based data receivers, computer vision cameras, digitized purchase records in medical stores, and other tools to capture this data from different areas in order to continue to retrieve this data in real time on a daily basis.

With the help of AI-powered data analytics tools, health officials can determine demand trends for specific drugs in each smart city area. Using this processed information, along with the information and predictive analytics provided by AI tools and applications, public health agencies can direct the supply of these drugs and equipment to areas where demand is greatest. higher. More importantly, local authorities can use dynamic pricing strategies to subsidize drug prices for these drugs in areas of high demand.

As we know, some medical conditions affect people of specific races or regions more negatively than others. For example, hemochromatosis, a metabolic disorder, is most often found in people of Irish descent. Likewise, more African Americans suffer from sickle cell anemia and myeloma than people of other races. AI allows public health authorities to quickly infer such trends from billing information compared to standard analytical tools and methods. As a result, government agencies can lower prices and increase the supply of drugs needed to combat such health conditions for the people or communities who need them most.

Use AI-powered cybersecurity tools to protect health records

Healthcare-related data breaches tripled in 2021 compared to previous years, according to a study. Additionally, ransomware attacks in the healthcare industry in the United States resulted in losses of nearly US $ 21 billion in 2020. To prevent such attacks in the digitized public health industry, a combination of big data and AI can be used by public health agencies. AI may have a few issues in healthcare, but it remains the most useful resource for preventing data security breaches and other types of cyberattacks in digital healthcare. Public health agencies can make the most of big data with AI-powered data analytics tools. Such tools are typically used for greater speed and clarity in detecting threats in a digitized health data network.

As any cybersecurity expert would tell you, regular patch and update management is helpful in warding off many cyber threats. However, the most powerful attacks must be dealt with proactively before cybercriminals can gain remote access to public health systems. Such malware attacks can cause severe damage to healthcare IT infrastructure at a much faster rate than the response generated by cybersecurity experts in public healthcare facilities. Many organizations increasingly rely on AI to autonomously make cybersecurity-related decisions by studying attack patterns for different types of network threats. An immediate response is useful to prevent the spread of a cyber attack to multiple devices.

AI cybersecurity tools rely on comprehensive machine training data to understand the different types of attacks and how to reverse them. Such tools can then differentiate “abnormal” data flows from normal data flows in a network. Differentiation is usually the first step in AI cybersecurity. AI can identify even the most sophisticated threats created by cybercriminals. Even if they enter your network, they can be prevented from causing further damage to the network. Once AI detects an attack, it can orchestrate other data protection devices and applications to collectively take action to prevent it from progressing. The importance of big data and AI in cybersecurity is especially pronounced in public healthcare during the ongoing pandemic, where overburdened hospitals need all available data to treat patients.

Accelerate medical research

Clinical trials are an important part of medical research. The inferences derived from these trials allow pharmaceutical companies in the public sector to develop new drugs, machines and therapies. Today, AI has advanced to the point where scientists and health researchers can use it to speed up the process of performing such trials. As we know, when COVID-19 first erupted around the world, there was a ton of data generated from various sources on the virus itself, its transmission, possible mutations and other details. . Big data and AI have played a key role in harnessing all of this information, which has proven to be useful for vaccine development in record time.

Along with accelerating clinical trials, big data, computer vision and AI form a valuable combination to streamline cancer diagnosis and treatment, as well as genomic diagnosis. Typically, private organizations spend more on AI implementation to leverage big data analytics in healthcare functions. Given the large number of benefits and applications of computer vision, AI, and natural language processing (NLP) in private healthcare, smart city governing bodies can also deploy these tools to Fully harnessing big data in healthcare in smart cities.

Besides these applications, big data can also be used by public health insurance bodies to calculate the amount of insurance premiums for policyholders. To calculate the premium, large amounts of data regarding factors such as patient eligibility, use of strong drugs, and past medical records are studied before determining how much an insured will need to pay in installments. While public health has always been a priority for governments around the world, the emergence of COVID-19 has shown us how much more remains to be done. One of the grim achievements of public health authorities today is that a huge number of deaths caused by the virus could have been avoided had the pandemic been managed more competently. Yet governments around the world can use the experience of the past 18 months as a valuable benchmark to more widely include the digitization and analysis of big data to protect public health. While AI and big data have played their part in the development of COVID-19 vaccines, public healthcare can be dramatically improved in smart cities by involving these other two in the industry.