The COVID-19 pandemic has triggered an acute need for healthcare providers to avail the assistance of technology solution providers that offer solutions based on industry-wide regulations like FedRAMP.

Nowadays, IT and tech solutions are developing applications keeping compliance in mind. Healthcare providers and public agencies can implement such digital applications to address the challenges. However, the best digital healthcare application is the one that has gone through the best testing process. We help you know this by discussing the best testing approach for healthcare applications in the context of FEDRAMP.

This blog might be of your interest: Mobile Application Testing Solutions For New-age Mobile Applications

About FedRAMP

The Federal Risk and Authorization Management Program (FedRAMP) is a risk-based approach to adopt and leverage the cloud by the federal government. Established in 2011, it is intended to empower enterprises with new-age cloud capabilities. A cost-effective approach, it emphasizes security of federal information, authorization, and continuous monitoring of cloud’s performance and its products.

Why FedRAMP?

Some of the reasons explaining the importance of FedRAMP are as below:

• FedRAMP governs the security and risk assessment of cloud by providing a set of standards.

• Any application of cloud by a public agency must comply with FedRAMP standards.

• As a rigorous process, FedRAMP evaluation offers a standardized approach to security assessment, authorization, and continuous monitoring. Each of these requirements must be satisfied to prevent falling out of compliance.

• FedRAMP effectively helps reduce duplication and inconsistencies and facilitates cost efficiencies.

• FedRAMP establishes a public-private partnership to promote innovation and the advancement of more secure information technologies. With FedRAMP, you can forge public-private partnerships and drive innovation for more secured IT framework.
• FedRAMP enables the federal government to accelerate the adoption of cloud computing by creating transparent standards and processes for security authorizations and allowing agencies to leverage security authorizations on a government-wide scale.        FedRAMP helps create transparent standards and processes which agencies can leverage and scale with ease. This feature of FedRAMP accelerates cloud adoption by agencies.

Authorization through an agency and process in FedRAMP: 

The Agency Authorization process allows agencies to work with a CSP (Cloud Service provider as and when required. When a CSP decides to work with an agency to avail Authority to Operate (ATO), they get to work with the agency throughout the FedRAMP authorization lifecycle. Listed below are the  different processes that go into the authorization process.

• Readiness Assessment –To achieve the FedRAMP-ready tag, it is essential on the part of CSP to partner with a certified 3PAO (Third Party Assessment Organization). It is then only that completing assessment of readiness would be feasible. The RAR (Readiness Assessment Report) records the capability of CSP to meet security requirements.
• Pre-Authorization–  CSP establishes a formal partnership with an agency through requirements specified in the marketplace. In this process, CSP has to ensure that the leadership is committed to implementing FedRAMP process. Moreover, it has to ensure the security categorization. The last step is to initiate the kickoff meeting so that background functionality and technical security are ensured and there are not compliance gaps.
• Full Security Assessment –Before this process, it is expected that SSP is complete and approved by the agency. An independent audit is conducted by 3PAO with an aim to test the CSP’s system. Security Assessment Report (SAR) are prepared, based on which CSP develops POA&M (Plan of Action for achieving Milestones)
• Agency Authorization Process –. This process involves the agency conducting a review of security authorization package. CSP initiates remediation as per the results of the review. Finally, the agency customer drives risk analysis before issuing an ATO. The CSP and 3ATO then uploads all requisite security material to FedRAMP’s secured repository. This is followed by the review of the security assessment materials to be included in the marketplace.
• Continuous Monitoring – All the agency customers must be kept informed and updated with security deliverables. So, CSP ha to provide periodic deliverables for security. This includes vulnerability scans, annual security assessments, significant change requests, incident reports and updated POA&M. Through the services, each agency conducts periodic (monthly and annual) monitoring of deliverables.

Strategy for compliance testing

Indium will follow several strategies in its digital assurance solutions to perform various compliance tests to ensure the application security. Below are the types to testing requirements involved during testing for FEDRAMP requirements.

Web Application/ Mobile Application /Application Program Interface (API) Testing:

• Cloud Access – Roles associated to the cloud access and access limitations.
• Configuration – Web server configuration level validation.
• Authentication and Session Management – Validates how session is created between browsers/devices and maintains session state.
• Data Storage – Verify Encryption outside application platform.
• API – Perform internet searches to identify any publicly available information on the target web application and make sure all input elements are validated.

High-level Testing activities to meet the FedRAMP Req.:

Check this out : Automation Testing for Financial Services Application

Network & Penetration Testing:

Below is the process that will be ensured as a part of network and penetration testing to ensure the FEDRAMP requirement

• Security assessment schedule
• Describe Tools for security controls
• Injection Analysis & Broken Authentication / Authorization (API & Web)
• Vulnerability Assessment & Penetration Test on API (request & response) & Web.
• Vulnerability analysis on API, APK & iOS

Stages in Vulnerability assessment:

Tools to validate:

The post Best approach to test Digital Healthcare applications for FEDRAMP appeared first on Indium.

The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.

According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:

Predictions about AI/ML in Business

• Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
• Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
• Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
• Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.

Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:

Use Cases for AI/ML in 2022

• Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
• Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
• Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.

Also Read: 10 Promising Enterprise AI Trends 2022

AI/ML Paving the Road Ahead for Growth

In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.

AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.

The post AI & ML: Forecasts and Trends for 2022 and beyond appeared first on Indium.

How are healthcare organizations leveraging predictive analytics to derive actionable insights from their ever-growing datasets? We find out here.

Staying ahead of Patient Health Deterioration

It is the most essential application of predictive analytics in healthcare.

It helps caregivers react quickly to any change in a patient’s vitals and gather foresight into possible deterioration before symptoms are evident.

A 2017 study demonstrates this: at the University of Pennsylvania, a predictive analytics tool using machine learning and EHR data helped identify patients vulnerable to severe sepsis or septic shock a full 12 hours before the onset of the illness.

Predictive insights are particularly valuable in the intensive care unit (ICU), where timely intervention can help save someone’s life and prevent patient health deterioration.

The increased adoption of wearable biosensors offers manifold benefits, too, for care providers. They enable remote health monitoring and help detect early symptoms of health deterioration.

Preventing Patient self-harm

Early identification of individuals likely to self-harm will help provide the essential mental healthcare to avoid potentially serious or fatal events.

According to the World Health Organization, almost 800,000 people die of suicide each year, which is one person every 40 seconds.

Studies have showed that predictive analytics, using electronic health record (EHR) data and depression questionnaire, helps identify individuals at higher risk of committing suicides or other forms of self-harm.

In a study led by Kaiser Permanente (a leading American healthcare provider) and conducted together with Mental Health Research Network, EHR data combined with a depression questionnaire helped accurately detect those with a higher risk of attempting suicide.

Another study, featured on the American Journal of Psychiatry, aimed to build and validate predictive models with the help of electronic health records to predict suicide attempts and suicide deaths after an outpatient visit.

Based on predictors such as prior suicide attempts, mental health substance diagnoses, mental health and more, it was found that within 90 days of a mental health visit, suicide attempts and suicide deaths among individuals in the upper one percent of predicted risk were 200 times more common than those in the bottom half of the predicted risk scale.

Predicting patterns in patient utilization

Predictive analytics helps healthcare organizations ensure adequate staffing levels for busier clinic hours, minimize wait times and improve patient satisfaction.

With the help of big data visualization tools and analytics strategies to model patient flow patterns, healthcare centers can ensure the inpatient department has adequate beds available for patient admission, that the outpatient and physician offices have enough resources to reduce patient wait times and manage workflow and scheduling adjustments accordingly.

Scheduling changes help nurses and doctors cope with the increased patient flow while reducing the burden on them, thus ensuring they provide timely care and improve patient satisfaction.

Data Security

Predictive analytics and artificial intelligence (AI) play a key role in boosting cybersecurity, with the sophistication of cyberattacks (involving malware, phishing and more) rapidly on the rise.

Confidential patient information worth big money, a vast network of connected medical devices, outdated technology, among other factors, make the healthcare industry a constant target of cyberattacks.

Predictive analytics tools and machine learning help calculate real-time risk scores for different transactions and requests, making the system respond differently based on how the event is scored.

David McNeely from the Institute for Critical Infrastructure Technology says: “Once the risk score has been determined in real-time, the system can use this during a login event to either grant the access for a low-risk event or to challenge for Multi Factor Authentication [MFA] or possibly block the access for high-risk events.”

Create risk scores for chronic diseases

Early identification of individuals with a higher risk of developing chronic illnesses is essential for two reasons. It gives care providers and patients the best chance of preventing long-term health issues. It also helps mitigate the potential cost and complexities of the treatment.

By creating a risk score—from examining patients with identical characteristics, gathering lifestyle and clinical data and using algorithms to understand how various factors effect patient outcomes—healthcare providers gain insight into the type of therapy and wellness activities which can benefit their patients.  

Summary

As far as health management is concerned, prediction is the foundation for prevention and treatment. Predictive analytics helps healthcare providers in different ways. In addition to those mentioned above, the technology helps identify individuals likely to miss a clinical appointment and send timely reminders, manage supply chain to enhance efficiency and cut down on unnecessary costs, develop effective therapies and new medication, improve patient engagement and more.

Given its manifold benefits, it’s no wonder that, according to a 2017 study by the society of actuaries, 89 percent of healthcare providers were then either already using predictive analytics in their organizations or planned to in the next five years.

The post Top 5 use cases of Predictive Analytics in Healthcare appeared first on Indium.

Businesses spend tons of money on marketing to acquire new customers and increase customer base. However, businesses can save a lot by just retaining existing customers. Yes, according to customer retention stats , 5% boost in customer retention increases the businesses profit to 25% to 95%

Some of the industries that are affected by churn rate are insurance companies, online streaming services, ecommerce, subscription services (Gyms) etc. In this blog, we are going to consider the example of Insurance companies. This blog will showcase the struggle insurance companies face to retain their customers and how analytics can help address the issue.

Who is a lapsed customer?

A lapsed customer is who has not come back to buy in an expected amount of time. In this case a customer who does not renew his/her insurance policy. This happens due to many reasons. The customer might not be happy with the service, they might get a better service for the same price or they do not like a particular product.

No matter what the reason is, it is your responsibility to bring them back, otherwise, you are leaving money in the table.

Luckily, with advanced analytics, you can target certain customers and create specific marketing campaigns to encourage them to buy from you again.

Analytics Case for Lapsers

How to methodically attack the lapsers problem?

As an Insurance company, you will collect a massive amount of data from your customers. Not just customer data, but also the transaction data with multiple product lines of general insurance, life insurance, health insurance & medical insurance etc.

The problem here is that the data sits in the computer and most businesses are not capitalizing on the benefits it can bring to the business.

Lapsed customer is one of the big problems the insurance industry faces. With the help of advanced analytics, insurance companies can not only solve the lapsers problem but also target the right products to the right customers, thus bringing more revenue.

To demonstrate, let us divide the customer base into 3 segments and Strategize accordingly

1. Lapsers – Customers who have moved out of the system. We have to strategize a plan to win back the lapsers
2. Likely to Lapse – Customers who are most likely to move. We have to retain them.
3. Loyal Customers – Customers who have a low likelihood to move. Cross-sell other products to these customers

Lapsers – Win-back Strategy

The best strategy to convert a lapsed customer into a loyal customer is by reaching out to them and persuade them with similar products. You must figure out what will be the “next best product” to sell to the lapsers.

Fortunately, analytics can help in finding the right products for lapsed customers.

Find the next best product with the help of a collaborative filtering recommendation technique. This technique will help you by filtering out items that a customer like based on reactions by similar customers.

Keep in mind, use a limited set of 3-4 recommended products rather than all products to win back the customer. Suffocating your customers with all your products will not help you achieve your goal.

Likely to Lapse – Retention Strategy

The next step would be to identify the customers who are likely to lapse and make sure they are not leaving you. To identify that,

• Build a lapse model to find the likely lapsers.
• Take the top 30-40% high probability lapsers and try retaining
• Retain using the same product or Sell another product

Loyal Customers – Cross-sell Strategy

Loyal customers are the ones who are most likely to stay with you. These customers like your products and will be ambassadors for your brand. The best strategy for them is to cross-sell your products.

What product to cross-sell to loyal customers?

Use association rules to increase cross-sell conversion. The Association rules method will be apt for uncovering the relationship between variables in large databases. This rule-based machine learning method will help in the cross-selling strategy.

Once you have the data and plan, all you have to do is make sure that cross-selling campaign is run successfully.

Customer who buys ‘a’ GI and ‘b’ TI also buys ‘c’ Insurance

In a Nutshell

This blog explained the use case of Insurance industry alone. However, analytics can be used across multiple industries. In the future, more and more businesses will use predictive analytics to forecast events and gain actionable insights that will help them in making the business better.

By using analytics, you are not only getting a competitive advantage but also save time, resource and money in the long run. After all, data is only a strategic asset when you can put it to work. Analytics will only help in identifying whom to target with what, it is the organization’s responsibility to make sure the best products and services are delivered to their customers.

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