All great cultures are empowered and metamorphosed by the wisdom of generational planning. Native Americans, for instance, believed that one must act keeping in mind the welfare of the next seven generations following their time: a tenet of homeopathic practice and avowed even today by modern science. These cultures understood the pitfalls of analytical myopia and the need to think ahead of time to realize subsistence and more.

To dilettantes with only a passable knowledge of this subject, the term ‘predictive genomics’ may seem obsolete since all endeavors in the field of genetics are predictive in their intent. However, it is a discipline at the crossroads of various fields like personal genomics, phenology, and bioinformatics alongside many more. Its relevance lies in the understanding of the human phenotype (expressed traits in an individual) as a function of the person’s genotype and his/her environment and being able to integrate the two variables of the function in a population for generations using advanced computational modalities like AI and Machine Learning.

Much of today’s predictive genomics is a result of Genome wide Association studies (these studies were possible due to the rise of biobanks, safekeeps of data sets demonstrating great genomic and trait variation) done over the course of the last two decades, occluding further doubts on the inheritance of complex traits, narrowing them down to the thousands of genetic mutations that are known as SNPs (commonly pronounced ‘snips’). These SNPs are the fundamental unit of biological evolution of all organisms, according to neo-Darwinian theory; mutations in their myriad iterations can make or break a species in the long run as they can be at the core of the inheritance of new survival traits and speciation or the accumulation of traits that lead to extinction. They are random and continually occur around us.

Predating these discoveries, the human genome project was completed, about 20 years ago, bequeathing scientists with a huge amount of data; experts estimate that research in genomics could yield up to 40 exabytes of data. With such a staggering volume of complex data to navigate, AI and ML techniques become obvious candidates for enhancing the efficiency and accuracy of predictive genomics.

Today, pharmacogenetics powered by deep learning algorithms make for an excellent use-case of digitally powered predictive genomics. A study that seeks the correlation between drug response and the individual’s genotype, its research labs sequence and genotype millions of people to understand drug delivery and inhibition across different populations. Such data is groundbreaking in demonstrating the relevance of individual gene expression in the efficacy of various medicines.

Consider the gene CYP2D6, expressed primarily in the liver and is crucial for the metabolization of codeine, one of America’s most popular pain relievers. The gene codes for the synthesis of proteins that converts xenobiotics like codeine to water-soluble morphine through demethylation; it is worthy of note that codeine has no analgesic action by itself. Now, we know through statistical analysis that 1-5% of Americans is poor metabolizers of codeine to the point it elicits no response, while another 1-21% are ultra-metabolizers, leading to huge spikes in blood morphine levels but effective for a very short interval of time. Similar trends can be noted for hundreds of drugs as they are all metabolized by CYP2D6, with its 161+ recognized haplotypes(alleles) dictating the effectiveness of these drugs.

Today, we are struggling to fully comprehend the effects of these known gene combinations, let alone those of the numerous rare haplotypes formed through mutations (SNPs) found across the world further convoluted by, ironically, the uniformity of variation across ethnicities. it is however important as predicting the functions of these novel alleles is key to improving the drug responses of these patients.

It is at this juncture that deep learning becomes relevant as the next machine learning paradigm. By implementing techniques that combine CNN image analysis and transfer learning, we can build deep neural networks that can generate voluminous sequences with known variations spiked into these sequences. Once we generate scores for each allele, we can train a model to assign these scores, forcing the CNN to ‘learn’ key sequence features. The experimental data on rare variations can be used to refine the final layers of the network, which can then predict and assess the outcomes of using codeine in individuals with rare alleles of CYP2D6.

Looking ahead

One must not overlook the fact that ML in genomics is in its infancy; a field that is less than a decade old. The reason is that the 3-D relationships shared by genes are much more complex than pixels and their interactions; as mentioned earlier, image recognition and analysis have great potential in the ML world. Today, breakthroughs in research have led to the union of both these techniques: ML devices like Deep Gestalt can accurately diagnose( up to 91%) over 200 disorders through image analyses by observing facial phenotypic manifestations of genetic aberration(note that the utilization AI/ML in genomics corresponds to deep learning and that remains so for the scope of this discussion, as well).

Deep learning neural networks are of 4 types broadly and they use different inputs; while fully connected networks use k-mer match metrics, convolutional and recurrent networks can use DNA sequence data, as well as image and time-series measurements respectively. The fourth type, graph convolutional, utilizes protein-to-protein interaction and structure. These are often manifest as modalities that can identify sequence context features capable of predicting transcription factor binding, decoded networks revealing differential gene expression, and more.

Applying Deep Learning to arrive at this profound understanding of gene mutations can be pivotal in understanding the origins of tumors and the development of gene therapy for cancer prevention and the prevention and treatment of many genetic disorders. It can cut down on years of painstakingly slow research and help us arrive at possible solutions much faster, or it can help us choose the most promising solutions – potentially saving us millions of dollars that could be wasted by going down the wrong road.

Yet, these technologies have miles to go before achieving mainstream acclaim and widespread professional implementation; for now, scientific incubation in research labs under the eyes of experts is the best mode of development for predictive genomics.

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Over time, it became clear that the spatial geometry of the molecules is of more relevance than their chemical composition, opening the floodgates of the vast world of synthetic drug design. The role of computers began here, as digital techniques like Computer Assisted Drug Discovery (CADD) enabled us to expedite laborious database searches to identify possible matches in 3D structures, helping us discover much smaller molecules of comparable geometry. With the surfeit of organic molecules growing steadily every day, we need computing tools that can rummage through databases to find appropriate candidates in drug design.

Pharma’s dependency on database searching makes it a prime candidate for the implementation of quantum computing, owing to its ability to catalyze CADD. But before we delve into that, let us take a closer look into the labyrinthine world of QC, hoping to accrue a working knowledge of its method.

QC: The rudiments

The rise of quantum mechanics is arguably the greatest development in science for the better part of the last century. It has been key in solving some of the great challenges of modern physics, but its application in IT comes as a surprise to many. Simply put, the quantum mechanical innovations, both theoretical and practical, have helped reimagine the smallest unit of information in computing- the ‘bit’.

To better understand this, we need to look at story of computers and the transistors that form their core. Around the turn of the 21st century, scientists at the Silicon Valley were working towards reconceptualizing the transistors through a drastic reduction of their size, with 14nm transistors being the norm today. At this level, transistors may begin to prove ineffective as electrons start exhibiting quantum mechanical behavior via quantum tunneling, marking a technological dead-end for computational progress, which in turn reflects as the end of technological progress.

However, scientists were able to nullify this roadblock (or here the lack of one) through the revolutionary quantum computer model. Here, the binary bits are replaced by qubits, which, while remaining binary in theory, exist in a superposition of various quantum states. The values of a qubit can be of any level 2 quantum system, like a spin or magnetic quantum number, or a single photon in its state of horizontal or vertical polarization taking values 0 or 1, mirroring their classical binary counterparts. Courtesy of the bizarre rules of the quantum realm, the qubits do not need to be in either states independently; they can exist in various proportions of both these states simultaneously until observed(illustrated in the Schrodinger’s cat thought experiment), owing to the property of superposition as mentioned above. It may be looked at just like when the binary photon passes through a polarizer (the observation); it can only have one orientation. As a result, the superposed qubits can store vast data points as compared to classical bits; consider a combination of 20 qubits in superposition: this would give us 1,048,576 values, all of which can be used for computation once the superpositions are collapsed.

The computational process must be juxtaposed with its classical colleague to understand what we’re dealing with: while a normal logic gate works with various inputs to give a single output, the quantum gates can now use superposed qubits to entangle them, and simply measure the values to collapse them to their binary values. The sheer speed that this method promises is self-explanatory. This means an exponential acceleration in computation, reflecting in various aspects of pharmacology, from discovery to clearance and marketing.

The R&D ramifications of QC
The scope of QC in pharma is not limited to, but lies predominantly in drug discovery, namely the understanding of structure-property relationships and identification of stereoisomers to create matching proteins that are smaller in molecular weight and helping identify the spatial structures of target molecules. One key area where this may have a pivotal impact may be in the assessment of how proteins fold, a phenomenon that may happen in a plethora of permutations and combinations. The quantum simulations can predict molecular structures with unprecedented speeds, but also provide us with highly accurate models. Some of the most intense research in this area happens now in creating QC models for target identification and hit generation, with mixed results. And while converting some of these real-world problems into quantum mechanical ones are onerous, it is as clear as day that quantum computers, in the right hands, are a handful in powering technology to its next stage.

For now, it may suffice to say that the future of QC in pharma reposes in a superposed state of probabilities, and only time can open the box to reveal what the true nature of its reality holds for the progress of medicine

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While it has been common for suppliers to stop measuring themselves post the delivery of a product, the emergence of reverse logistics means a new variable in customer satisfaction. Although this is not to suggest that it is a novel aspect of retail – large retail businesses have been managing product returns even preceding the e-commerce era. However, the see-saw battle between various corporations on the said digital platforms necessitates that companies put forward and reverse logistics at par on their priority list. Yet, the simple truth is that many myths and misconceptions cloud executives’ views on strategies for reverse logistics. And even though studies predict phenomenal benefits to ramping up this department, only a minuscule number of companies name it an essential investment.  

Apart from the one obvious reason which is volume: the number of transactions in reverse is usually far lesser that forward logistics. 

Why do retailers continue to let reverse logistics play the second fiddle? 
It is, essential to identify and solve some of the major puzzles in reverse logistics.   

1) Lack of dedicated team  

Most firms do not boast a sole reverse logistics department. It is, many a time, assigned with an underqualified individual/team discrediting its importance as a domain of itself.  However, the Inability to monitor the condition of products returned, percentage of sales lost, customer base lost, secondary revenue loss etc., could break the foundation of your enterprise, especially  if the returned products can be repaired or repurposed, scrapped for parts, or directed to the secondary market.   

2) Categorisation of the returned products as trash   

As cited earlier, this is one of the biggest misconceptions of the modern retail industry. At the same time, less than 20% forms the bulk of all defective returned products; even defective products can find value through proper processing. Analytics has shown that the number one reason customers return items is remorse, not defect. The key to reclaiming value from returned goods, damaged or otherwise, is the implementation of streamlined and effective reverse logistics processing.  

For instance, we know that the reverse logistics chain is initiated the second a customer clicks return on the purchased product. Therefore, the initial assessments must happen at this instant, making an effort to understand the cause of the return to make an informed guess on how it can be dealt with to ensure your company’s losses are minimal.  

3) Absence of tailormade reverse logistics software  

It is appalling how every subsequent point made in this analysis leads back to the others; here, we see the consequences of a common myth in action: ‘managing returns is simpler than running distribution.’ Nothing could be further from the truth. 

 The fact is that  returns management is far more complex than distribution. Returns management requires executives to work with buyers, operations, sales, accounts payable, and systems. While the leadership on the distribution side needs to care only about the safety of their loads as a whole, the reverse logistics manager must inspect every item in detail. In the case of returns, the OEM has to grant authorization before shipping. F 

Most distribution centres employ WMS to expedite these tasks to manage the large volumes, and the returns are synchronously processed through the same software resources. This creates complications because the software is just not designed to hadle the unique workflows and level of detailing required for Reverse Logistics management   

Warehouse management system for reverse logistics  

While your firm’s standard WMS will encounter difficulties in processing forward and reverse logistics, it is possible to build them to maneuver in both operational areas seamlessly.  Much rides on the symbiosis between the WMS and the enterprise resource planning systems (ERP). This combination works in a customer-oriented manner, maintaining what looks like a thorough customer record, complete with the profile of all transactions between the company and the customer. The ERP platforms, paired with a warehouse management system, allow companies to track returns easily, monitor the movement of goods, issue credits through the accounting system, and update everything in the customer record.  

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Blockchain:  

While bitcoin has always been a headliner, few recognize its driver technology known as Blockchain. It is an advanced technology utilizing a peer-to-peer ledger of records that is of virtually incorruptible safety. As it is designed to be self-managed, it is highly affordable, barring the initial capital requirement. It can tread great lengths in fixing the apparent intangibility customers feel towards their insurance plans and policies. Due to its unique and secure trademarks, it could increase processing speed, help establish customer trust, and expedite claim processing without falling prey to fraudulence and manipulation.  

Artificial Intelligence: 

While AI needs no introduction, its deployment in InsurTech brings to the table a host of unprecedented gains that can revolutionize the face of the financial domain. Moreover, with personalization now being the norm for everything, AI has much to offer in creating bespoke experiences, helping customers feel discerning, and bolstering their trust.  

With adoption levels and implementation still slow, experts believe that its initial impact will be in the automation of underwriting and claims processes. In tandem with machine learning, it will analyze risks and identify new sources of capital that could lead to new frontiers in checking fraud and money laundering.  

Gamification:  

This is undoubtedly one of the most peculiar and yet promising trends of InsurTech in 2021, showcasing the adoption of video-game-inspired strategies like level clearing and unlocking, performance bonuses, and so on. Although rare, some actuarial companies even deploy their games to promote their insurance-related products. It may also lead to a sense of brand loyalty to the firm.  

Younger clients often duck the insurance business due to its labyrinthine and precarious nature; with this customer category, gamification has yielded the most results. Moreover, firms can utilize the aspects of gamification at every juncture in the customer experience, unlike the other trends that limit themselves to technological aspects.  

Machine Learning: 

Although machine learning is related to AI (while being highly specific), it is only with its integration that InsurTech can achieve total efficiency. While artificial intelligence can improve and speed up claims processing, it is only with machine learning that we can automate it. The power of pre-programmed algorithms will be a great tool in automation using the digital files accessible via the cloud. And its utility is not limited to either P&C firms or their customers; it supports both entities almost equally. In addition, it has numerous other payoffs, like risk calculation, CLV, and PIE computation.  

IoT: 

With the ever-rising number of connected devices around the world, insurers can use these tools to harness data; the open-source data available from smartwatches, homes, and even automobile sensors can help collect pivotal data that can assess the customer psyche. A simple use case is the collection of health data from a client’s fitness smartwatch, which could furnish the P&C insurers with information that would help in policymaking for the said client. Though it has come under the scanner for potential infringements of customer privacy and corporate fraud, this trend continues to thrive and grow with some of the top insurance companies in the US today.   

If you are looking for a Digital Product Engineering Services partner who not only keeps up with emerging trends but takes giant strides in the creation of their own, blackrock is the right partner for you. Browse our website to see more on our FinTech products.  

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The current state of Port Management 

While this illustration may project the massive volumes that are the everyday norm in international maritime traffic, the rather mundane management solutions employed by some of the world’s busiest ports do not mirror the stir in the industry; with the majority of ports worldwide classified as Tier 2 and 3 – where operations are primarily manual and modern equipment and resources are slow to come, making these government-owned ports sluggish and stagnant. In an industry like transportation, businesses toil to improve efficiency, even if only by an iota, as they recognize how far it may go in giving them the edge, putting them in the best position to have their competition by the hip. In other words, improved efficiency in maritime transportation translates to immediate positive transformation in the global economy.   

While improved turnaround time and efficiency as a payoff of greater automation seem quid pro quo, it is also crucial in optimizing safety and security. With the current traffic and competition in maritime trade today, port executives must work infallibly to ensure smooth function; slight errors from the employees can lead to a slew of issues, like stalling and even severe accidents owing to carelessness and mismanagement. Therefore, it is as clear as day that apart from infrastructural development, the development of the IT resources and improved automation holds the key to accelerating progress and boosting revenue.   

In an ever-changing environment, which involves operations from various corners of the globe, all with individual maritime policies and regulations, manual management and administration will continue to become more complex with every passing year.  

The power of the Maritime Single Window  

This problem could be resolved if port management were empowered by IT with end-to-end visibility for mariners, improved transparency, and data sourcing through IoT and RFID. Yet, the most tactile feature would be streamlining complex procedures (compliance, safety, etc.) by connecting all the governing agencies and offices through one portal. In addition, it must account for the differences in policies and rules of various countries mandated by the governments of the user.   

Enter the Maritime Single Window (MSW), the portal that makes cross-border trade simpler and marine logistics chains more streamlined, mandated by the IMO in 2019, and yet to see marked adoption rates worldwide. The myopic view of many ports fails to see the harmonization of vessel reporting obligations possible with it and the customs process solutions. We can link the governments involved in direct trade electronically for the first time, providing tangible cost savings for businesses and the government. And for governments, it is easier to check and eliminate corruption by improving methods to counter dishonest practices and reducing discretionary decisions by simply automating them.   

The benefits they offer to both traders and port managers are myriad and can be listed as follows:  

For traders  

• Single submission – With suitable national registration, companies must enter the required information once rather than submit paperwork at every stopover.   
• MSW can take over the clearance procedures as they are facilitators of complete and accurate data to said cross-border agencies.  
• It can win maritime traders higher credit scores from banks, improving their financial positions.  
• More efficient logistics: Companies can get cheaper logistics and transport services because MSWs facilitate services connecting transport and logistics chains.   
• Reduced business transaction costs: Companies can interact with the standard import and export service ecosystem with lower prices and higher efficiency with an MSW. This may reduce the recruitment needs of companies within their international trade staff, thus saving human resources and management costs.  

For Ports & Regulatory Agencies   

• Connectivity, simplification, standardization, automation, and increased reliability of information exchange and data reuse  
• Ease of transactions resulting in lower transaction costs.   
• Facilitation of G2B status information.   
• Improvement of port logistics.   

Future-proofed shipping  

The verdict has to be that MSW is the future of port management and logistics; it galvanizes and bullet-proofs shipping for all parties. It is ever scalable and can be reproduced over multiple ports and their terminals. And while their adoption is straightforward, stakeholders must carefully modify their business strategies to stay clinical amid the changes brought within the system. When implementing the single window system, care must be taken to maintain the legacy systems, information flows, and processes to create a viable trade ecosystem that benefits all parties equally.  

If you wish to transform your maritime business and desire a trustworthy partner in doing so, browse the blackrock website to read about xPort, our pioneering MSW solution. 

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With little competition and a fresh market, this may be the best time to set up an on-demand fuel delivery business, especially in a world emerging from the shadow of lockdowns and the automobile industry seeing a resurgence.   

The Building Blocks 

With the global gasoline/diesel requirements consistently outweighing the capacity of fuel stations over the years, on-demand fuel delivery is bound to be an imperative in the coming years. However, with that said, one must play their cards right at the very beginning, partnering with a reliable IT solutions provider, securing compliance from governmental authorities, and so on.   

The next step must be to build your fleet, equipped with the resources for everything from transportation to maintenance and safety. Businesses must do it with a keen eye on the reasons for the very existence of this market and the audience the firm specifically caters to. For instance, companies must hire HAZMAT (Hazardous Materials) drivers, who are elite drivers involved in the transport of flammable material. Digital Solutions that complement the fleet must also be in place simultaneously (real-time tracking systems, forward-looking camera systems, IoT sensors, collision mitigation technology, and the like).  

It is also vital at this juncture to address the towering safety concerns involved in transporting highly combustible liquids and gases over significant distances by road. The companies must immaculately monitor the transportation tanks to ensure safety; they must appropriately allocate funds to ensure this, without which the company’s existence will perennially hang by a thread.   

Only then must intrepid business owners venture into creating the on-delivery app to enable the service, though last is not the least. The usability shortcomings of the application are hurdles many entrepreneurs fail to cross, with order tracking and route management being the Achilles’ heel in these solutions. The architecture of the app backend is another crucial hallmark as end-users will use the app over data or Wi-Fi all the time. Thus, the seamless delivery operation is staked almost wholly on some key app features that demand discussion.   

Vital features of an On-Demand Fuel Delivery app: 

• Pricing charts  

The customers must get a glimpse of the savings they make through properly illustrated price charts; the introductory offers may be the bait for most customers. However, no compromise must be made on fuel quality, with double-filtered fuel as the norm.   

• Real-time tracking  

Given that the orders for fuel delivery often are in emergencies, the two-way tracking system will be equally valuable to the delivery agent and the customer while also winning the customer’s long-term trust. In addition, they can use third-party maps that show the delivery routes with ETA.   

• Accept/Reject options  

Since safety is paramount in an on-demand fuel delivery service, the fuel delivery agent must have the right to reject orders if they are inaccessible or compromise security in any manner. There must also be an option to keep charges pending until the delivery agent can make an informed decision.  

• Synced with various payment options  

      The customers should be able to make their payments over a broad range of payment methods at their convenience. They must be aware of the various situations the customers may find themselves in and be adaptable to COD, online banking, and the slew of mobile banking apps with whom they must ensure compatibility. There must be an order/transaction history so the users can keep track of their expenditure online.   

• Customer support  

The business must address the fears and misconceptions of their customers by establishing communication channels between the people and expert employees who can mitigate their doubts and create clarity over the ins and outs of the delivery protocols. Automated messages can also have a positive impact in this regard.  

blackrock recently built a digital platform for on-demand fuel delivery for a US-based early-stage enterprise. You can read the complete success story here. 

If you have an idea for a big change in the automotive world and need the right IT support to help you realize it, drop a mail to sales@blackrockdxb.com.  

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An unprecedented time that saw many non-emergency clinics shutting down around the world, millions of people who relied on these facilities (de-addiction centers, autoimmune disease therapy, and dentistry to name but a few) were in distress. While our science fiction compels us to dream of fusing our consciousness with machines and turn into immortal cyborgs, the cold hard truth is that we continue to be vulnerable to our environment, and are simply too mortal. 

This drubbing we suffered at the hands of mother nature was a rude awakening for the world of healthcare, no different for the IT world. However, this has been a phase of renaissance for health IT and the mHealth domain; adoption rates have skyrocketed since the beginning of lockdowns in the US. Studies conducted in Asia bolsters the faith, with all respondents in Malaysia claiming that they use one health/wellness app or the other, with 78.6% saying they are currently using COVID-19 related applications. Malaysia unveiled its flagship TraceTogether app in its fight against the pandemic, the first of its kind across the world as early as March 2020.  

It is therefore important at this juncture, to understand mHealth at a rarefied level. In understanding its role we must probe its 3 main functions today: 

· to connect citizens with the health system and provide superlative technological innovation 

· to support teaching, training, and related health activities in an exemplary manner 

· this point is specific to the pandemic; to control the rampant spread of COVID-19 and provide mHealth solutions for the same 

Virtual care             

It is necessary to make a distinction between telemedicine and virtual care. While telemedicine refers to the remote treatment of a certain health condition, virtual care is a bracket term for all remote interactions by healthcare professions to engage with their patients. It could include even the act of maintaining feedback from a patient over mail. Recent times have witnessed a jump in the proliferation of apps with this function; they may include risk calculators, fitness trackers, remote patient-monitoring, pharmaceutical e-commerce, and many more. While their payoff is monumental, data security continues to be the elephant in the room. People must realize that while they do indicate primary diagnosis, it is important not to make clinical decisions and adopt treatment based on these diagnoses; the apps themselves must take steps to alert the users on the same to prevent misdiagnosis and other mishaps.  

Digital therapeutics 

Presumably the most exciting area in virtual care, the phrase evokes futuristic archetypes in the mind, which unfortunately is no coincidence; experts predict that It may take another decade to develop technology that can provide measurable and not to mention, the sustainable outcome in patient health. Though today, we are offered nothing more than a cracker to quench our thirst, it is still exciting to see the quantum leaps we have made. While we are struggling to integrate technology into the treatment and therapeutic methods of most diseases, researchers have made enterprising inroads in treating mental disorders through DTx.  

Look no further than be award-winning gameChange app, with its ambitious goal to treat psychosis patients through VR. The addictive and engaging nature of digital media can act as an antidote for people suffering from mood disorders, manic depression, and the early stages of many neurotic disorders. It may even be argued that remote counseling is an ideal option for people with psychological disturbances, as they can be engaged within the comfort of their dwellings. 

Implementation 

It is important to remember that while a healthy person demands a million things, the sick man desires the fulfillment of a lone wish; empathy for the patients is what most companies give little thought for. mHealth apps must be built with minimal ads and distractions that can sway the interest of clients, and must constantly reinvent and update themselves for a seamless patient experience.   

While we embrace the future for what it’s worth, our efforts must augur with the collective patient psyche; they should feel tangible and personal, even to the Luddite, and personified by the client, and maintain a physical and psychological relevance that can materialize into observable therapeutic progress. 

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Since the correct diagnosis can be the difference between life and death, the incorporation of IT calls for several prerequisites that ensure the safety of the patients; though they are many, it comes down to the logistics. For instance, a cluttered UI for a diagnostic tool could be counter-intuitive and become a detriment to the data integration due to operational difficulties. The tendency to unreservedly accept data from machines can taint the judgment of even the most experienced medical practitioner, leading to faulty diagnoses. The tools must also act as an enhancement to workflow, which would automate mundane tasks, and expedite tiresome, inefficient procedures, while never increasing physical or cognitive workload. 

When the above criteria are fulfilled, IT empowers the medical industry to identify and classify diseases, serve to operate medical equipment or analyze the risk levels of patients towards developing specific diseases. Today the AI revolution has enabled us to look beyond the horizon and witness the endless possibilities it offers for the medical industry.  

While some are merely self-explanatory, it is worth discussing some of the new trends in this seminal field. 

AI image integration 

The diagnostic scene utilizes a handful of scanning and imaging tools for investigation, most of which are not helpful in diagnosis by themselves in more complicated cases of trauma. For instance, a routine bone contusion, usually self-managed, may also imply micro trauma to the local soft tissues which may lead to pain and exacerbations in inflammation. While an X-Ray can help diagnose the bone injury, the muscle damage is discernable only through CT-scan probing. For a holistic treatment program to be synthesized, a juxtaposed interpretation of the two images would be key; it is here that the power of sensor fusion can be harnessed to combine the data. To enable efficient analysis of patient scans, image recognition AI software should be able to combine and interpret data from different imaging sources to gain a better perspective of the patient’s pathology. This could generate concise insights into disease/injury progression, providing radiologists with a higher level of understanding of the condition of patients. 

Early diagnosis  

While we can implement software to make critical diagnoses for a certain pathology, trends today show that this can be scaled to work for multiple disease diagnosis systems as well.  

It is important to note that the technology at hand is more than commendable; the latest studies show that a neural network can be trained to diagnose a given pathology faster than the average radiologist. Yet, the standard software and equipment employed by most healthcare and diagnostics services may be bypassed by rare disease/disorder morphologies, which are ultimately misconstrued by the algorithms and thereby leading to misdiagnoses. Optimistic predictions for the future signal the formulation of neural networks that can deduce a plethora of conditions from a single image (or any other kind of data set); this would be a huge step in diagnosing rare conditions like genetic and autoimmune disorders. Though the process for such diagnosis would be arduous, requiring a superlative level of skill in radiologists, it is projected that such broad-spectrum software will soon replace the single pathology algorithms, owing to their greater applicability and financial viability in the long run.  

“Neutral” algorithms for compatibility 

The introduction of new AI software in medical diagnostics facilities is viewed as turbulence in the hospital’s routine workflow, thereby cast as a deterrent to their productivity(a significant reason for low adoption rates), since the installation and setup may become a difficult task. This is why companies today are making concerted efforts to develop universally compatible software that can be fed into the radiologists’ set up with minimal hassles. This is becoming increasingly common today as most all FDA-cleared algorithms are vendor-neutral, meaning they can be set up in scanners and devices across brands and models. 

Today, giant strides have been made in implementing this software directly into diagnostic devices like scanners (MRI, CT-scanner), which can help the automation of medical imaging using RPA. 

This has been achieved, for example, with MaxQ AI’s Intracranial Hemorrhage (ICH) technology being embedded into Philips’ Computed Tomography Systems. The potential downside is that employees have little to no freedom of choice in the software they use; if the embedded software does not meet their requirements, they must opt for cloud-based software. However, the financial edge of software-embedded devices cannot be overlooked, as the machines are more efficient and can bolster the financial returns that come with them.  

As we move towards a future where AI model architecture becomes less convoluted, and ironclad data security systems with HIPAA-compliant technology streamlining the safe access to patients’ medical data, the implementation of relevant technology would be the factor that dictates the success or subsistence for diagnostic service companies.  

Even at the individual level, one can utilize remote diagnosis technology, which is becoming increasingly popular in providing primary diagnoses to ensure timely interventions and treatment. 

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Here is a selection of the benefits offered by Robotic Process Automation in the banking sector.

1) Minimal initial capital demands

Since the concept of bank automation is alien to many, it is important to tackle the issue at the rudimentary level. Since RPA is one of the simpler branches of Artificial Intelligence (in conjunction with Intelligent Automation), the existing IT infrastructure of most established banks prove sufficient to reap the benefits; the financial institutions consequently have a very low risk-reward ratio. Though banks are not always up to date on their IT resources, the legacy systems of these banks can take care of most RPA implementations. Since these services are outsourced, little to no skill is required in operation; this mitigates the need of extra manpower and thereby, extra costs.

2) Scalability

Although this may be viewed as a follow on from the first point, it is worthy of mention. RPA bots are built to be stable and vastly scalable. Once the firm identifies its objective clearly (reduce cost or labor, saving time and so on), scaling of the available technology becomes smooth. In fact, when powered by the faculties, they can handle leviathan volumes of organized data: for instance, consider the issue of data consistency that financial establishments have to maneuver; like the ever-changing details in personal information held in the databases of banks, like the spelling of a person’s name, their address or financial details. While a slight variability can make conventional systems go for a toss, the empowered Robotic Process Automation systems can process such information with no hassles, thanks to its prodigious, yet rigid order. This is what makes RPA head and shoulders above the rest and key to its scalability is the fact that its machine learning features, when incorporated, are resilient and robust.

3) Speeding up processing

Speed and efficiency are synonymous for finance companies since customers need timely service for their requirements and delays nurture the risk of losing customers. When opening an account, for example, the verification process can be tedious for many. With RPA, the procedure can be expedited and accelerated exponentially. The robots can deal with everything from the validation to the delivery of the account to the client on completion. The same could be said on loan and mortgage applications, or the repetitive aspects of AP accounting, which when carried out by machines, frees up the employees to garner complex solutions involving reasoning and creativity. Thus, by optimizing performance and eradicating human error, RPA offers us a win-win; banks can take the adage of “time is money” literally and enjoy the reward of satisfied customers. Rread Experion’s success story here.

4) Promote intelligent banking strategies

The application of RPA in finance could bring in key foresights and vision into banking. Many of the top companies today are using AI analytics to procure a bird’s-eye view of the market, tracking unexpected changes and shifts in trends to offer their customers the most optimal investment options, or in fixing the most competitive interest rates; the role played by RPA in this regard is monumental. This is reassuring for customers as they offer maximum output with little risk.

While we are on the topic of risk, it is imperative to discuss the uses of RPA in fraud detection and safety. According to Thomson Reuters, banks spend almost $384 million on KYC process compliance every year on average, and many are moving to RPA to analyze customer data. Software bots run checks on customer data to assess and confirm KYC compliance, or to ascertain a customer’s credit score, which helps to keep malpractice at bay. If suspicious activity is intercepted, they can employ account freezes or other means to curtail frauds.

In conclusion
In an age where AI is growing and proliferating at a rate unseen hitherto, banks find themselves in a do-or-die situation, where adopting superlative technology is necessary to stay in the hunt. With the current rates of adoption of RPA in the finance realm, if banks wish to stay relevant and competitive, they must critically analyze their position in the market and make paradigm shifting changes in their strategy. They must be on the lookout for the top IT service providers to be ahead and make impactful strides in the industry, the effects of which will show us the way to the future.

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Data security: The nemesis? 

The client information ensconced in telehealth databases are some of the most concise, wide-spectrum and relevant clusters of personal data, one of the most valuable on the black market. The consequences of a mishap, spanning from privacy contraventions and insurance claim threats to bioterrorism of cataclysmic potential, imperils the future of telehealth. At the personal level, looming fears of information leak among clients lead to non-disclosure of information deemed private, leading to incomplete diagnoses and ineffective treatment. In a not-so-distant future, where even medicine administration would be carried out through intelligent intravenous systems, ironclad security systems are imperative to effectuate any quantum leaps in telehealth.  

For telehealth solutions to be secure, service providers must embrace the basic tenets of data security, keeping in mind that every mechanism is only as strong as its weakest link. The telehealth software must be robust, with means of secured channels between the client and the service provider.  

Healthcare organizations need to secure their endpoints, maintain compliance with standards such as HIPAA, look for high-performance options while choosing software, establish policies and conduct training for relevant practitioners to ensure there is no theft or misuse of information and most importantly, identify a reliable technology provider to leverage their industry expertise to their advantage. 

However, the measures taken by the telehealth providers are futile unless consumers also partake in security; the clients must be trained in safe practices, like the use of tough passwords and VPN, not merely for telehealth but all applications on their device(s). Customers must also be vigilant of fraudulent service providers – identifying services with standards such as HIPAA compliance would be a good start. 

Low adoption rates  

Another major obstacle in the telehealth industry is the slow adoption rate among the elderly, who constitute 56% of all health expenditures in the US. This is because of the disengagement of the sense of touch, which, according to experts, is the most reassuring in the doctor-patient relationship. A generation that does not feel up to speed in the tech-savvy world, the elderly are made to feel ill-at-ease, left at the bidding of apathetic robots, and thus always feeling the need for human touch. 

It is problematic getting to the root of this issue, as it is humanistic and dynamic to each individual. However, telehealth companies can certainly benefit from making their services more intelligible; user-friendly wireframes and self-explanatory UI can go a long way. The most successful telehealth companies of today succeed in bringing about a sense of human touch, through the application of psychologically compatible virtual guidance systems, which would assist the patients through their routine tasks such as exercise and medicine administration. 

It is pivotal to focus on identifying and resolving social constraints through IT, an approach that has been formulated over years of experience.  One of our projects with a major telehealth service based in the USA comes to mind, where the use of simple UIs was decisive in doing away with clients’ dependence on bystanders, hitherto a necessity in managing the patient’s day-to-day activities. A good example of this is an intuitive solution we built for patients with neurodegenerative disease – you can find the full story here. 

Telemedicine and Scalability 

A look into the issues of telehealth would reveal, at a mere casual glance, how its various problems are inscrutably interrelated, with one issue seemingly originating from another. It is within this labyrinthine system that the problem of scalability lurks, the antiparticle of the challenges mentioned earlier. The solution which would be the key to the future of telemedicine, is also the most tedious, as the solutions must be sought from the perspectives of technology, economics, and culture; the data security hurdles challenge the information storage potential, while the low rate of adoption makes it difficult to scale the infrastructure of the services. 

The mindset of potential clients can also be a tough nut; the consultation rates are frowned upon by traditionalists who dismiss teleconsultations as mere gimmicks employed by hospitals to extort money from immobilized and immunocompromised individuals, and therefore, opt for home consultations. Even now, these are the alternative to consultations at hospitals, while teleconsultation remains the last resort, employed mostly in counseling services and minor health concerns.  

The construction of intelligent and robust solutions capable of handling large volumes of data is key here. Another one of our projects handled this particular challenge rather impressively, allowing radiologists to diagnose and process over 600 reports a day.  

As we strive to mitigate the numerous other hindrances in scaling telehealth, we adhere to the rudiments of product engineering and envision molding the future of healthcare through digital healthcare solutions, where its accessibility will only be exceeded by its economic value, catapulting us to a new level of health and well-being. 

If you are looking for a reliable IT partner who understands the landscape of digital health and has a diverse portfolio of healthcare projects under their belt, we might just be the right pick for you. If you would like us to accelerate your vision for digital transformation in your organization, drop a mail to sales@experionglobal.com. 

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