Health & Wellness - Healthcare Security & Compliance

AI applications in healthcare are advancing rapidly, with potential applications being shown across various domains of medicine and healthcare. However, there are currently limited software testing tools and strategies available to test AI-based healthcare products.This article explores the key areas, main challenges and limitations, and approaches of Software Testing of AI-based healthcare domains and considers the steps required to improve current testing strategies.AI applications in healthcare are advancing rapidly, with potential applications being shown across various domains of medicine and healthcare. However, there are currently limited software testing tools and strategies available to test AI-based healthcare products.This article explores the key areas, main challenges and limitations, and approaches of Software Testing of AI-based healthcare domains and considers the steps required to improve current testing strategies.The growing potential of artificial intelligence in healthcareArtificial Intelligence and Machine Learning seem to be the new slang of the 21st century. PwC, a professional services firm predicts that AI will add $16 trillion to the global economy by 2030.AI-ML are general purpose technologies capable of affecting entire economies. It outshines in recognizing micro and macro patterns insignificant to humans and can be very useful.Ever since the probability and idea of making machines learn by themselves came into existence, its applications have been used in almost every sector of the economy and the healthcare industry is no exception.AI nowhas the ability tofind patterns in massive volumes of data that are too complex for humans to notice. It accomplish this by merging data from a range of sources, including linked home devices, medical records, and increasingly non-medical data.AI is finding huge recognition in the field of healthcare andmedical diagnosticsfor the past few decades. However, one section in the healthcare industrythat is relatively new to the use of AI is the verification and validation of medical devices. With the demands placed ontesting applications and reliability towards delivery teamsincreasing exponentially over the years, it has become more important to take a step beyond just automation and start usingAI and ML for medical device testing.Testing of medical devices is a long and important process that should be carried out simultaneously throughout the development process. Integrating AI and ML into this process can be advantageous in many ways, and here are seven major areas that can gain the most from it:1. Data-driven insights: As more and more data is being made available for general processing anddata insight generation,decision science is now mostly driven by calculating usage of AI and ML.Platforms and tools for medical device testing are becoming increasingly available to ferment data in a short period and derive meaningful insights, making it available in real-time.These AI tools can be used during product verification and validation to identify complex scenarios for testing from the requirement traceability matrix.2. Creating test cases:Test cases are mostly designed by highly skilled test and automation engineers. This needs a combination of versatile skills and collaborative effort across teams. By using AI toolstest cases can be generated automatically which takes multiple factors into consideration like functionality, scalability, coverage, loading.AI algorithm has the ability to look inside the code to derive test cases that have a higher probability to uncover defects compared to the manual approach.The useof AI has led to powerful increase in the pace of test development.3. Bringing intelligent automation to testing:Instead of running tests and fixing the bugs manually AI-driven test controllers can be used to identify test case failures and run repetition steps to cover multiple regression cycles in accordance with the type of fault detected. It helps to increase the automation coverageby approximately 30% when using AI. 4. Improving system agility:One of the primary reasons why automated tests fail is not for their lack of quality but the lack of their swiftness keeping with the changes that are taking place. AI-powered testing tools can be designed to learn from test data generated using the emerging Machine Learning process so that test automation systems can adapt quickly to system changes.5. Self-healing capability:Testing is a continuous process in a software life cycle. Organizations spend around 15 to 25% of their time maintaining automated test cases. A self capable system driven by AI can be a great tool to reduce the burden on an ever increasing testing budget as the system grows to be more complex. It is usually observed that about 60 to 70% of all defects reported can be addressed by employing AI-powered solution.6. Minimizing manual labor:Manual testing of medical devices can be an laborious task as it involves several regulative requirements. AI helps to reduce manual testing efforts at some steps by bringing analytic functions using a combination of image and other sensors hence improving the speed and accuracy of testing. It has been found that the use of AI in testing reduces product maintenance costs by almost 40%.7. Strict testing procedures to prevent diagnostic errors: Diagnostic errors lead to 60% of all medical errors. As AI can offer more accurate diagnostics there is always a chance that it can also make mistakes, which sometimes causes companies to hesitate about adopting AI in diagnosis.The use of AI and ML in medical device testing has its pros and cons, however its benefits outweigh some of the challenges associated with it.Software testing in AI and MLThe core element of developing ML and AI algorithms are testing. You may compare this with common unit testing of the application testing. The AI/ML engineers develop an AI algorithm and verify that the training data does a qualified job of accurately classifying or regressing data with good generalization. Test Engineers also use some validation techniques which are like test data of software testing.AI-based software uses algorithms and data which are mainly working together to show the results. If the algorithm's validation phase gets wrong parameters then it might affect the results which we are looking for. To get more accurate results the test engineers needs to revisit the algorithms themselves, change the parameters if required and rebuild the model. This might be compared to the system test which the tester was doing to understand the behaviours of the system.Testing approach on AI-ML based Healthcare DomainsA common healthcare domain testingis a process to check healthcare applications with factors like safety, compliance, accuracy and cross dependency with other entities, etc. The tester ensures that the standard quality, reliability, performance, safety, and efficiency of the Healthcare application on its place and software behave as expected. Current AI-based tools and software accompany algorithms and logical tests which Al engineers already did.However, the challenging part for the tester is to check how the algorithm behaves within the software and therefore the system. QA teams got to have strong domain knowledge and backgrounds on healthcare systems, algorithms and the way these two works together.Mostly healthcare algorithms are pretty complex and challenging to predict for common software testers. The algorithm goes through training and testing sets thus creating some meaningful data associating with human behaviours. An insufficient or incomplete data set or low-quality data can cause biases within the solution. A system is over-trained to ascertain the same thing or isn't trained enough to form an accurate judgment.Another challenge that testers face while they're testing AI-based medical systems is that the amount of data required to test the system. Approaching restricted data items won't provide statistical assurance of the system. That opens another challenge for testers on what kind of skills should a tester have and the way they should interact with complex systems.Mainly testers are using boundary testing and dual coding to resolve most of the problems associated to complexity. Testers got to have some data knowledge and familiarity with Algorithms would be an essential skill.Sometimes the algorithm used, huge data volumes or solution complexity, testing these systems are often as complex as the solutions itself. It requires extensive technical and data science expertise from the testers making the AI tester's job different from any other manual or automation testers.ConclusionSoftware industries may face a spread of challenges when using AI to test the healthcare applications or medical devices for quality, including identifying the precise cases, a scarcity or lack of understanding about what really must be done. Verifying applications behavior based on data input, testing application for functionality, performance, scalability, security, and more.In conclusion, AI-based healthcare products will get complicated day by day and as testers we got to be ready to test one of the most complex algorithms and logic, potentially saving lives and protecting the people.

Increasing prices, discerning consumers, and disruption by technology are a few challenges health and life insurance companies face. So, can technologies like blockchain and smart contract solutions development for healthcare enable systems to address insurance issues? Health and life insurance companies are some of the key players trying to incorporate emerging technology blockchain and smart contracts. They are doing so to change record keeping, transaction execution, and interaction with stakeholders. The question is whether blockchain technology and its applications can facilitate insurers to gain efficiency in healthcare insurance operations. Can they enable them to reduce costs, manage vulnerabilities, enhance customer support, expand their business, and thus, improve the bottom line? Let's find out.What are the Challenges with the Current Healthcare Insurance ServicesLife insurance policy life cycles with complex touchpoints and healthcare insurance claims often involve complicated and unpleasant experiences.It makes the establishment of good insurer/client relationships problematic. Term life insurance appears to be a static, one-dimensional product. Whether the policyholder dies, it pays out, or when the contract expires, it lapses.Otherwise, there is no engagement until the sale is closed except for premium payments. With no legal standards demanding its acquisition and little encouragement other than the provision of death coverage, term life insurers continue to be hard-pressed to accelerate strong revenue growth. In reality, life insurance (and maybe even health insurance) tends to be low-return, high-cost products for those who are younger and healthier. They provide little relevance to the everyday life of a policyholder.What Solutions Does a Blockchain-based System ProvideThe basis for incorporating a wide range of wellness-related activities into the insurer/client dynamics can be EHRs safely stored on blockchain's immutable database and powered with smart contracts. In this context, technology can not only be used as a secure archive of past medical history to allow faster underwriting and pricing. It can also store near-real-time information about the lifestyle and health of the policyholder through telematics devices tracking their day-to-day activities. Also, Read |Blockchain Smart Contracts in Insurance | Advantages, and ApplicationsBenefits for a life insurerIn this direction, life insurers can reevaluate a person's risk profile and modify coverage costs accordingly while providing incentives like premium adjustments, exercise or dietary performance discounts, or perhaps even gamification-driven competitions constantly. The architecture of blockchain can incorporate the fast-growing number and types of health and wellness data sources more effectively than a more conventional, widely distributed, and often fragmented communications infrastructure. An interoperable blockchain-based health record system can safely update in near-real-time with diversified, lifestyle-related data points. Further, it can drive more regular revaluations of the vulnerability and enable dynamic premium pricing of life insurance schemes. Potentially, life insurers can harness this gathered information to incentivize risk-reducing behavior individually, thus, facilitating customers to make more knowledgeable, healthier lifestyle choices by aligning with them. They can also provide promotions to related vendors (gyms, fitness facilities, spas, etc.). Carriers may also be in a stronger position to deliver unbundled, customizable, personalized policies. Also, Read |Augmenting the Management of EHRs (Electronic Health Records) with BlockchainBenefits for a health insurerIn turn, healthcare insurers can use such telematics data to facilitate wellness services and trigger premium discounts. The collection of such real-time data connected to smart contracts in health insurance can alert policyholders to take prescriptions and schedule check-ups. Fluctuations in a patient's health factors like heart rate or blood pressure can set off alarms. This data collection will activate contact from a healthcare care provider or a wellness coach. When combined with advanced analytics and real-time data sources, blockchain-facilitated data storage can ensure owning life and health insurance is an essential part of a policyholder's lifestyle.Benefits for an insuredCurrently, consumers connect with their health insurance when it requires them to pay premiums or consult a disease or injury. Instead, they can establish a long-term positive relationship with their insurer. Exercising and staying productive can lead to obvious and direct benefits — like premium discounts or reduced deductibles. Then, consumers might equate health insurance with well-being and advantages rather than health expenditures. Effectively, insurers may benefit from building a healthier business book that is more risk-averse.Also, Read | Blockchain Solutions for Reconciliation and Dispute ResolutionHealth insurance-specific applicationsFacilitate Health services for insurersCreate a more reliable medical and wellness knowledge repositoryCause drug warnings or routine medical visits or diagnostic testsFoster healthy lifestyles, reducing the cost of medical complicationsAllow insurers to know their policyholders betterLife insurance-specific applicationsFacilitate faster evaluations of the underwriting and sellingCreate less discretionary, more realistic pooling of risksAllow programs geared towards risk reduction and lifestyle and longevity improvementsFoster revenue growth and augment loyalty programs to policyholdersAllow more customization and individualized coverageAllow insurers to know their policyholders betterBut a Nexus of Blockchain, Smart Contracts, and IoT is EssentialIncorporating real-time information might help mitigate arbitrary pricing decisions. It can set the stage for a more engaged customer relationship. However, blockchain alone can not accomplish this. Whether manually uploaded or automatically collected via the Internet of Things ( IoT) sensors, fine-grained lifestyle data points will need to be securely channeled and stored on the blockchain database and converted into successful consumer insights and rewards. Also, Read |Creating a Nexus of Blockchain, AI, and IoT for Business SolutionsChallenges with such a Blockchain-Healthcare Insurance SolutionMoreover, the possibility of tracking and penalizing those who do not practice healthy lifestyles 24/7 might cause some customers to refuse such deals. On the other hand, insurers could face additional regulatory restrictions to protect the interests of both users and non-users.ConclusionUsing blockchain and smart contracts to support ongoing, interactive, value-added customer relationships may encourage more individuals to buy coverage and stick to their insurer once they do so. It will be more applicable when points created in reward programs are at risk if a policyholder switches carriers. For more detailed information, connect with our experts.