Telehealth and Cybersecurity: 3 Best practices to ensure data security in remote patient care

Telehealth refers to the remote access and delivery of healthcare by integrating digital devices, healthcare equipment, and healthcare systems. The pandemic has accelerated the reach and adoption of telehealth.

There are many telehealth products in the market today. Here are a few examples  –

  1. Sesame care –  Provides listings of healthcare providers with affordable pricing, which varies with region. Patients can book appointments for the same day and for a future date.
  2. Klara – Enables real-time video visits between caregivers and patients. Includes scheduling appointments, insurance verification, sharing reports during the consultation, and documenting instructions for individual care. It also provides a virtual waiting room for the patient.
  3. HealthTap – Another telehealth product that enables patients and families to find an affordable virtual care provider for primary health.
  4. MeMed – Provides virtual healthcare in several areas such as general health, mental health, child care, etc. It works with businesses to develop solutions that bring down the cost of healthcare services for their employees. 

While there is greater adoption of telehealth services, there is reluctance from patients worried about their data privacy, while others are not comfortable going virtual. While the latter can be resolved through user education or caregiver-assisted consultations, cybersecurity is a bigger problem.

Read more: How to transition to telehealth

Access to healthcare and information from anywhere increases the threat surface and the associated security risk. With the increase in telehealth traffic, cyber-attacks have increased exponentially.  American legislation provides for the Health Insurance Portability and Accountability Act (HIPAA) which is the cornerstone of governance around healthcare. Any telehealth service must be HIPAA and HL7 (Health Level 7) certified as it holds patient data.

HL7 is a set of international standards around transferring electronic information on health between healthcare providers and related systems. One of its key protocols, FHIR (Fast Healthcare Interoperability Resources), defines how healthcare systems can share data,  irrespective of how it is stored in these systems. It is web-based, with REST being one of its standards. This makes it possible for easy integration with healthcare-based consumer apps providing lightweight data as per need. 

You may also like: FHIR – The winning edge for successful patient engagement

Three best practices to ensure data security in remote patient care are:

Multi-factor authentication of identity

Healthcare providers, patients, and payers can access health records outside the network via cloud-based solutions. Simple user/password authentication and authorization will not suffice in such scenarios. A hacker can quickly gain entry. Multi-factor authentication that requires at least two pieces of information to enable user access from different networks will significantly reduce potential threats. It could be a 2F authentication or token from a verified phone number associated with the account and a strong password. Biometric authentication using fingerprint, voice recognition, or facial recognition are other means of multi-factor authentication. 

Cloud Infrastructure security

Today serverless architectures take away the load from developers to scale their applications based on load, with the additional benefit of reduced costs. A serverless application or service gets triggered by different data sources, i.e., distributed cloud services. Each data source comes with its event data format, each with a potential data injection loophole. Additionally, there are no firewalls protecting these services. 

Adequate serverless security would include: 

  1. Least privilege access for serverless services. Default deny approach by the service being accessed unless the necessary permissions are granted.
  2. Implementing a Web Application Firewall to protect against attacks like SQL injection.
  3. Cloud Encryption (or cloud storage encryption) – Data is encrypted before being stored in files and databases so that only authorized personnel can access this data.
  4. Continuous checks on third-party integrations
  5. Continuous monitoring of services auditing logs to take quick action or prevent cyber attacks
  6. The shared responsibility model between the user and service provider keeps the environment secure.

Network and data security 

Telehealth services over the internet, insecure connections, and lag in security updates pose a considerable security threat to patients’ health data. 

Using a VPN that restricts access to users with the proper credentials is one way of preventing such issues.

Striking the right balance between security and complexity of use is essential. Having said that, there are other systems that a telehealth service would need to interact with. Specifically, remote devices and sensors are being used for monitoring a patient’s health. All of this data that goes to the telehealth service and out of it would need to be encrypted at rest and in motion. Ensuring the data is transmitted on SSL, and secured storage reduces the possibility of a leak. Enabling periodic anti-malware and virus scans will also check cyber threats. Over and above application and network-level security, patients must be educated on the importance of cybersecurity while maintaining a  relatively lower complexity.

Ensure cybersecurity for your organization

Cyber-security is a crucial parameter to telehealth adoption. Therefore to manage this, an organization could  – 

  1. Partner with cyber-security providers, 
  2. Set up the proper framework and governance around telehealth,
  3. Adopt the latest security measures and tools, 
  4. Enable security at the application level,
  5. Continuously monitor all devices, data handshake points, and the overall network.
  6. Have scheduled virus scans and update anti-malware installed.
  7. Ensure solid passwords and meeting ids while using third-party tools such as Zoom to not compromise the patient’s identity.
  8. Keep the patient and caregivers well-informed on the usage of devices, password management, and enabling virus scans on their devices.
  9. Have an eye out for new security loopholes cited in the market.

Reach us at Trigent to evaluate the existing and build new secure telehealth application(s) and apps for your organization.

Build secure and efficient telehealth applications for your organization. Call us now!

Discover how predictive analytics is helping insurance companies minimize risk and fraudulent claims

As new technologies forge their way into diverse sectors, it comes as no surprise that the insurance sector is leveraging them for different reasons. Predictive analytics has found an important place in the insurance landscape for its ability to make data-based predictions.

The impact of predictive analytics in insurance sector

While predictive analytics helps the insurance industry gain great insights into customer activity and behavior, it also plays a massive role in preventing fraudulent claims and minimizing risks.

As per the Insurance Fraud Bureau, there has been one insurance scam every minute during the U.K.’s pandemic. Things are equally bad in the United States, where insurance fraud doubled to $100 billion last year.

The insurance industry is now moving quickly to mine data and track new rackets quickly. Explains Zurich’s head of claims fraud Scott Clayton, “By deploying the proper analytical tools, you can extract and interrogate the data, and use algorithms to highlight these links. By joining all the dots, you can soon identify persistent and prolific offenders.”

The pandemic’s unprecedented nature has set the tone for intelligent business practices that can shield them from fraud and help them strike back. Thankfully, predictive analytics, in tandem with big data, have answers to most of the problems insurers face.

How predictive analytics in insurance is minimizing fraud and risks

Understanding predictive analytics

To determine its role, we need first to understand that predictive analytics is an analytical tool that studies historical data to predict upcoming events and ensure business practices’ effectiveness. It gives organizations a competitive advantage and helps them stay abreast of changing trends. It looks into the data collected from different communication channels to analyze client interactions, agent feedback, customer behavior, etc., to build a more intelligent, data-driven ecosystem for all.

It’s no secret that insight-driven insurers are always better positioned to strengthen their capabilities in all five areas, namely, people, process, data, technology, and strategy. Predictive analytics helps them excel on all these fronts. 67% of those who recently participated in a Willis Towers Watson survey reported a reduction in expenses and a 60% increase in sales due to predictive analytics. Most importantly, it helps prevent insurance fraud.

The role of predictive analytics from a fraud prevention perspective

Insurance fraud has a significant bearing on the entire business, specifically on underwriting, and also causes a negative social impact. While undetected frauds drain finances and lead to many more scams in the future, those detected damage market reputation, and trust. Not to mention the legal issues that arise from them and the subsequent impact on future policies, procedures, and guidelines.

Predictive analytics helps insurers in the following areas to prevent fraud:

  • Pricing and risk mitigation – Offer insights that facilitate decision-making and estimate the level of risk that the insurance company has to assume while calculating the premium. For instance, those who go to the gym regularly may be eligible for a discount on health insurance.
  • Trends tracking – Helps insurers create new products, design new customer experiences, and deploy new technologies by keeping an eye on what’s trending in the world of insurance. This also gives insurance companies a competitive edge.
  • Fraud prevention – Helps insurers prevent fraud at different levels of the insurance cycle, including application, premiums, claims, etc. It offers a sneak-peek into public records such as criminal records, medical history, and bankruptcy declarations to review data for detecting inconsistencies and preventing frauds.

Dealing with insurance fraud

What’s frustrating is the fact that insurance frauds today are highly organized and occur digitally. Insurance companies have realized that the only way to fight and prevent insurance fraud is through data mining, analytics, and algorithms based on patterns in fraudster behavior.

Digital algorithms that have been hugely helping in timely scam detection are based on data pertaining to:

  • Referral history – Experts have created algorithmic models to estimate the probability of a claim going beyond a threshold level referred to Special Investigation Units or SIUs. This model typically uses the historical claims data referred to as the SIU to determine the probability value. Investigation scores are then calculated using investigation scoring automation techniques to distinguish between good risk and bad risk claims.
  • Historically rejected claims records – Based on the belief that claims that have been historically rejected stand a greater chance of being denied for doubtful potential frauds, digital algorithms automatically scan through the claims using several parameters. Claim Risk Indicators such as a customer’s SSN, address, contact number, etc., are carefully scrutinized using clustering-based data mining techniques. Claims are then categorized as ‘clusters with high claim frequency’, specifying the level of risk.
  • Individuals/groups – Digital algorithms, in this case, are based on data about individuals or groups that make fraudulent claims repeatedly. Flags are triggered every time fraudulent entities are detected, and these flags help identify fraudulent patterns.
  • Social media profiles – Algorithms, in this case, take into account social media profiles and interaction patterns of individuals along with other details such as lifestyle, attitude, etc. It takes into account mismatches between actual profiles of individuals on social media versus their claims. For instance, if an individual has forwarded an accident claim but their social media shows them partying with friends, there is certainly a mismatch that needs to be investigated. Algorithms based on social media posts are also useful in demarcating networks or groups of fraudsters.

Hurdles on the way to insure the future

Fraud detection is no longer static, limited to place or time. Every time a new detail is added, insurers now run predictive analytics at multiple touchpoints to enhance their fraud detection capabilities. Their efforts are now proactive instead of reactive and a great deal of effort is being put into fruitful collaborations with brokers and third-party vendors to build clear channels of communication and information exchange. But the quality of data still remains a big challenge.

Going forward, the focus should be on reducing data volumes and increasing data quality while ensuring that it is readily available as needed. The funnel needs to be narrowed in a way that competent individuals carefully review the results from machine analytics.

There are legislative barriers, too, concerning data sharing and individual privacy that sometimes stand in the way of data collection. Predictive analytics, however, is helping insurers make the best of what they have by sifting through information pools to help them produce intelligent products for the future.

Empower your insurance business with Trigent

Embark on a whole new journey with Trigent with predictive analytics at the helm. We can help you redefine your strategies to enhance risk management and ensure your future. Our disruptive suite of tools and solutions can transform your insurance business into a data-driven, efficient, and secure ecosystem.

Book a business consultation to know how we can help you keep insurance fraud at bay. Call us today.

Keychain – An effective way of securing sensitive information on iOS mobile apps

Mobile devices are a significant propellant for modern-day digital technology. Mobile devices are compact and the quickest mode to establish instant communication, eclipsing long distances. The underlying element that enables this exchange between individuals using their mobile devices is data. It is data exchanged between two or more devices that make communication or any other activity possible.

The digital mobile technology baseline is a vast data collection, stored either locally or remotely, sharing numerous information fields for multiple purposes. Flexible Image Transport System or FITS is a digital file format useful for storing, transmission, and processing data that in common parlance refers to information or data related to people such as photos, audio, video, text formats, travel itineraries, or shopping details. Private data can never be seen in a silo. It has to coexist with privacy policies that ensure optimal data security.

Mobile devices are inevitable in modern life, and securing mobile data is a critical concern in a digitally-driven world for quite. While there is increasing awareness among individuals to safeguard their data, cybercriminals are always on the lookout for loopholes. There are umpteen safeguards in place to thwart cybercrime, but the slightest of negligence can be an invitation to trouble such as phishing or data theft.

This post is a DIY for iOS developers to enable Keychain wrapper to secure small chunks of data on applications and secure services.

Before we get into the nitty-gritty of setting up a keychain wrapper for your iOS app, here’s what Apple has to say about the keychain wrapper API.

‘The keychain services API helps you solve this problem by giving your app a mechanism to store small bits of user data in an encrypted database called a keychain. When you securely remember the password for them, you free the user to choose a complicated one.

Keychain – An effective way of securing sensitive information on iOS mobile apps

Keychain wrapper can be considered to be one of the most secure technologies to store sensitive data, such as passwords, authentication tokens, or session data. Its security stems from the automatic encryption of data that is enabled before the file is stored in the system without the need of building encryption algorithms.

Following are the details on how to use keychain wrapper in mobile application development.

Install SwiftKeychainWrapper framework either by cocoapods or carthage. Sample code for few of the common usages are as follows

Add data value to keychain:
let saveThe Data: Bool = KeychainWrapper.standard.set(“Some String”, forKey: “myKey”)
Retrieve data value from keychain:
let retrieveTheData: String? = KeychainWrapper.standard.string(forKey: “myKey”)
Removedata value from keychain:
let removeTheData: Bool = KeychainWrapper.standard.removeObject(forKey: “myKey”)

Developers can do the following in Keychain Wrapper:

“Service Name” – used to customize bundle identifier value. By default, app Bundle ID is used to store the data in keychain
Data can be shared between applications using keychain by “Access Group”
By default, all items saved to keychain can only be accessed when the device is unlocked. To change this accessibility, an optional withAccessibility param can be set on all requests to select the accessibility level desired
To access the data in keychain in all the different devices used by the users, we can synchronize the data in iCloud
Unlike “User Defaults” – Data stored in Keychain will not get deleted while app cleared from cache (or) uninstalled from the device

Are you looking at developing an iOS application with robust security features? Trigent’s expert developers with decades of experience in iOS app development can help you with end to end development of secure and robust applications. Here’s a solution that was developed with an emphasis on personal data security.

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