Using technologies such as AI and IoT for smart healthcare can be instrumental in preventing another global health crisis like the COVID-19 pandemic.
There are many who rightly believe that the COVID-19 pandemic could have been prevented if all countries had prioritized public healthcare more. A collective lack of preparedness, coordination, and empathy has allowed the virus to run rampant and become the global monster we know today. While governments around the world can be held accountable for handling the contagion poorly, they can redeem themselves by ensuring that such a situation is not allowed to repeat in the future.
The correct use of urban computing for healthcare data collection, sharing and analysis can prevent another global pandemic. The use of AI, blockchain and IoT for smart health applications enables governments to test, trace and isolate infected individuals and stop regional outbreaks before they grow bigger.
How Urban Computing Helps Prevent Pandemics
As you may already know, urban computing involves the usage of intelligent and connected technologies for citizen welfare. There are multiple ways in which technologies such as AI and IoT for smart healthcare can be leveraged to predict and prevent a large-scale contagion in smart cities.
1. Evaluating Viruses Originating in Animals
Most infectious diseases such as COVID-19 are caused by viruses that originate in other animal species such as bats, chimpanzees, pigs, pangolins, amongst others, before migrating to humans. Detecting them at an early stage gives healthcare experts a much-needed head start in their fight against a potential pandemic. Researchers at the University of Glasgow have developed a system that uses predictive analysis and genomic sequencing to evaluate the migration capabilities of viruses. The system uses machine learning models developed using data from viral animal genomes to predict if such viruses will jump from animals to humans and then from humans to other humans. Healthcare centers in smart cities can similarly adopt AI and machine learning for viral research. The early discovery of zoonotic viruses before understanding their ability to migrate to humans allows healthcare researchers to begin an early investigation and facilitate outbreak preparedness.
2. Tracking and Isolating Infected Individuals
Generally, fever is the first sign of a foreign virus affecting an individual’s body. Such individuals can unknowingly spread the virus to others in crowded places. Therefore, detecting and isolating them is imperative for public health authorities to stop an outbreak. Smart cities can use thermal imaging cameras in crowded zones such as train stations, subways, sporting events, and concerts to detect the presence of high-temperature individuals. Designated health officials in such places can then test them to see if they’re carrying a novel virus that may be causing epidemics in other cities, states, or countries.
3. Using Healthcare Resources Optimally
Urban computing enables smart cities to be hyper-connected so that information can be shared from one point to another in minimal time. Such information is critical for channelizing healthcare resources in infection hotspots in a smart city. As a result, the zones with the highest number of sick people during an outbreak can receive more doctors, medical equipment, and medicines. If the situation in such hotspots gets worse, public healthcare agencies can set up triage centers in a bid to save as many lives as possible. By isolating such hotspots from other zones, the spread of infection can be contained within them.
Data collection is as important—if not more important—as how it is used in smart cities for public healthcare. IoT sensors, computer vision cameras, and other data capture devices create highly useful data points in smart cities to collect health-based data such as body temperature, skin paleness, and other sickness indicators in individuals during a period of contagion. Based on the information received from such devices, public healthcare bodies can manage an outbreak efficiently.
SaaS pricing inflation growing 4x faster than market inflation
Inflation has dominated the financial news landscape in 2022. In many markets, the consumer price index (CPI), has reached its highest point in a generation. This growth in the cost of ‘things’ also applies to software.
Almost every organisation has come to rely on SaaS to conduct business, from communications tools like Slack and Zoom to productivity suites like Microsoft 365 and Google Workspace, as well as department-specific platforms like Atlassian, Workday, NetSuite or Salesforce.
This is according to a report into SaaS inflation pricing from Vertice, a SaaS purchasing and spend management platform.
Spending on SaaS products grew more than tenfold between 2010 and 2020, from $13b to $157b annually. Investment accelerated even faster at the onset of the coronavirus pandemic, as companies raced to support remote working. SaaS spending increased by 26% in the months following the initial lockdown in 2020 and has only continued to grow in the years since.
Unlike many other significant overheads, like payroll and rent, the selection, management and renewal of SaaS are decentralised in nearly every organisation. This is for a variety of reasons, but buying power plays the most important role. Buying power typically sits across several individuals and departments, with finance leaders managing budget requirements, IT teams assessing systems and compliance considerations, and department heads selecting based on functionality. It’s a complex web of decision making and, even with the best intentions, it can be a struggle to gain a single view of all of the SaaS products a company uses.
This ‘wild west’ of a cost centre is a significant problem when the share of the total cost is considered. A growing percentage of all expenditures for businesses goes to SaaS, with around 12.7% of total spending now used on software investments. That means $1 in every $8 that modern organisations spend is now dedicated to SaaS. To translate that into dollars — as of 2022, companies spend around $3,112 per employee each year on SaaS. This figure rises to $4,552 for technology companies, who spend more than firms in any other category.
It has taken only five years for average SaaS spending to double. Based on the economic inflation rate over the same period, it would take 18 years for the cost of SaaS to double. This growth has far outpaced the rate of general economic inflation, even after factoring in recent periods of an uncharacteristically high CPI.
Clearly, the impact of SaaS in terms of productivity, collaboration and inclusion has been significant – but the accompanying cost has also been quietly spiralling upwards.
Analysis of more than 10,000 SaaS contracts shows that 74% of vendors have increased their list pricing since 2019. Among the quarter of vendors that have not, almost all have reduced the size of the average discount afforded to customers – effectively raising the spend without touching the list price.
A comparison of regional inflation rates with the SaaS inflation rate by geography reveals that over the past five years the cost of SaaS for US organisations has grown 3.5x faster than the general inflation rate – even after accounting for an exceptionally high national inflation rate in 2022.
SaaS inflation has outstripped general inflation rates even more rapidly elsewhere; spending at British and Australian firms has risen at a rate five times greater than regional economic inflation.
Joel Windels, VP of marketing at Vertice, said: “It’s become clear that not only is SaaS critical to modern businesses, but also that it represents a growing cost centre that can rapidly spiral out of control without strategic management. Even without investing in new tools or added licences, the data shows that spending on SaaS is exploding. With an uncertain economic outlook for 2023, finance leaders absolutely have to start taking a more considered approach to SaaS spending if they are to maintain growth and streamline their operations”