Healthcare

In an era where customer data is both vital and highly sought after, organizations must prioritize its protection more than ever. The healthcare sector, in particular, faces significant risks as its data is particularly vulnerable and often targets for cybercriminals. These offenders can profit handsomely by selling stolen healthcare information, making data security a critical concern. 

The American Hospital Association (AHA) strongly advises senior hospital leaders to view cybersecurity as a strategic priority that should be deeply integrated into the organization’s governance, risk management, and business continuity frameworks. The reason is clear: a breach in data security doesn’t just compromise information—it can delay treatments, disrupt hospital operations, and even put patient lives at risk.

In this blog, we’ll explore how the Zero-Trust Security Model offers a transformative approach to protecting sensitive healthcare data.

Key Takeaways:

With rising data breaches and ransomware attacks, Zero Trust has become a strategic priority, not just a security option. It shifts the focus from perimeter-based security to continuous verification of all users, devices, and applications—regardless of location.

According to IBM’s 2024 report, the healthcare sector faces the highest average cost of data breaches at $9.77 million per incident—highlighting the urgent need for robust security measures beyond traditional frameworks.

The increasing use of telehealth, remote care models, and IoT medical devices dissolves traditional network boundaries, making patient data more vulnerable. Patient health records (PHRs) are now 10x more valuable on the dark web than credit card information, emphasizing the need for stricter data access controls.

Cloud technologies like Identity as a Service (IDaaS), end-to-end encryption, and automated compliance tools are critical for Zero Trust. They simplify secure access, protect data in transit and at rest, and ensure continuous regulatory compliance, especially with HIPAA and GDPR.

AI-powered security systems proactively detect threats, predict potential breaches, and automate responses. They are especially effective in identifying insider threats, flagging suspicious behavior, and mitigating risks in real time.

Key Data Breach Statistics:

According to IBM’s 2024 Cost of a Data Breach Report, the healthcare sector experiences the highest average cost of data breaches—$9.77 million per incident—for the 14th consecutive year.

In April 2024, National Public Data, a data broker specializing in background checks, suffered a massive breach affecting approximately 2.9 billion records. The compromised data included sensitive information such as Social Security numbers, names, and contact details, potentially impacting nearly all Americans.

In July 2024, AT&T experienced a significant data breach when hackers accessed its third-party cloud service provider, Snowflake. The breach affected nearly all of AT&T's wireless customers, exposing call logs, text message details, and personal information.

Approximately 74% of all data breaches in 2024 involved the human element, including social engineering attacks, errors, or misuse, emphasizing the need for enhanced employee training and awareness programs.

Source

These staggering figures highlight the financial, operational, and reputational risks associated with inadequate data protection. Moreover, the rise of ransomware attacks targeting hospitals and healthcare systems adds another layer of complexity, compromising not just data but patient care itself.

What is Zero Trust, and Why is it Gaining Traction in Healthcare?

Zero Trust is a cybersecurity framework based on the principle of:

“Never trust, always verify.”

Unlike traditional security models that assume users inside the network are trustworthy, Zero Trust assumes that threats can come from anywhere—inside or outside the organization. It requires continuous verification of all users, devices, and applications, regardless of their location.

Why Healthcare Needs Zero Trust:

Data Sensitivity

Patient health records (PHRs) are 10 times more valuable on the dark web than credit card information.

Remote Care Models

The rise of telehealth and remote work has dissolved traditional network perimeters.

Regulatory Pressure

Compliance with HIPAA, GDPR, and HITECH demands stricter data access controls.

IoT and Connected Devices

Hospitals are filled with connected devices that can be exploited if not properly secured.

In 2023, 61% of organizations had a zero-trust initiative in place, up from just 24% two years earlier, according to a global Okta survey of information security decision-makers. In 2024, over 41% of technology and security professionals say they are in the advanced stage of zero-trust implementation, while 12% say they have achieved optimal maturity, according to a recent CDW survey of over 950 respondents.

Pillars of Zero Trust: The U.S. Cybersecurity Framework

The Zero-Trust Approach to Healthcare Data Security

The U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) has outlined five core pillars of the Zero Trust Maturity Model, offering guidance for organizations to build a Zero-Trust architecture:

1. Identity:

Focuses on verifying user identities with robust methods like multi-factor authentication (MFA) and biometrics.

Why it matters in healthcare: Prevents unauthorized access to sensitive patient data.

2. Device:

Encompasses IoT devices, medical equipment, mobile phones, and workstations.

Why it matters in healthcare: Secures devices that store or transmit ePHI, reducing the risk of breaches via compromised hardware.

3. Network/Environment:

Involves micro-segmentation to isolate sensitive systems and control traffic flow.

Why it matters in healthcare: Limits the spread of malware or ransomware across hospital networks.

4. Application Workload:

Focuses on securing applications and their data processing workflows.

Why it matters in healthcare: Protects EHR systems, telemedicine platforms, and cloud-based applications.

5. Data:

Ensures data is encrypted, classified, and protected both at rest and in transit.

Why it matters in healthcare: Safeguards patient records from unauthorized access and breaches.

Steps Toward Zero Trust in Healthcare: How to Implement Zero Trust Architecture

Here’s an actionable, step-by-step guide to help healthcare organizations successfully implement Zero Trust Architecture (ZTA) while enhancing patient data protection, ensuring regulatory compliance, and maintaining operational efficiency.

1. Assess Your Current Security Posture

Before implementing Zero Trust, conduct a comprehensive security audit to identify vulnerabilities in networks, applications, devices, and data storage systems.

Risk Assessment

Prioritize high-risk areas like EHRs, telehealth platforms, and IoT devices to identify potential external and insider threats.

Security Maturity Assessment

Use frameworks like NIST’s Cybersecurity Framework or CISA’s Zero Trust Maturity Model to evaluate your current security posture.

2. Define a Clear Zero Trust Strategy

A successful Zero Trust approach requires a well-defined strategy aligned with your organization’s goals.

Align with Business Objectives

Focus on patient privacy, regulatory compliance, and cyber resilience

Establish Governance

Create a Zero Trust steering committee with IT leaders, CISOs, and clinical stakeholders to guide implementation.

Prioritize Critical Assets

Identify “crown jewels” like PHI, financial records, and medical research data for heightened protection.

3. Strengthen Identity & Access Management (IAM)

In Zero Trust, identity is the new perimeter. Ensure that access is based on verified identity, not just network location.

Multi-Factor Authentication (MFA)

Require at least two verification methods, like passwords plus biometrics.

Role-Based Access Control (RBAC)

Grant access based on job roles to minimize unnecessary data exposure.

Continuous Identity Verification

Monitor user behavior for anomalies, even after login, to detect suspicious activities.

4. Segment Networks (Micro-Segmentation)

Reduce the attack surface by isolating critical systems into smaller network segments with strict access controls.

Isolate Critical Applications

Segregate systems like EHRs, medical devices, and payment platforms.

Network Access Control (NAC)

Control which devices connect to specific segments based on their security status.

Dynamic Access Policies

Apply context-based rules, like additional verification for remote access.

5. Automate Threat Response

Speed is critical when responding to cyber threats. Automate security processes to detect and respond to incidents instantly.

Security Orchestration, Automation, and Response (SOAR)

Use platforms like Palo Alto Cortex XSOAR to automate threat detection and response.

Incident Playbooks

Develop predefined response strategies for threats like ransomware—automatically isolate compromised devices and trigger alerts.

Automated Compliance Reporting

Generate real-time reports to meet HIPAA and GDPR requirements efficiently.

How to Modernize Healthcare Security with Zero Trust: The Role of Cloud Services

As healthcare organizations continue to embrace digital transformation, the integration of cloud services has become indispensable for modernizing operations, improving patient care, and ensuring the scalability of health IT systems. However, with this shift comes an increased risk of cyber threats, data breaches, and compliance violations. This is where the Zero Trust Security Model, reinforced by advanced cloud services, emerges as the key to safeguarding sensitive healthcare data.

1. Identity as a Service (IDaaS): Simplifying Secure Access

Managing user identities across multiple systems is complex, especially with remote staff, telehealth platforms, and third-party vendors. Identity as a Service (IDaaS) solutions like Okta and Azure Active Directory streamline this by providing cloud-based identity management that integrates seamlessly with both on-premises and cloud environments.

Key Features:

Multi-Factor Authentication (MFA)

Adds an extra layer of security beyond passwords.

Single Sign-On (SSO)

Simplifies secure access to multiple applications.

Adaptive Access Controls

Continuously monitors user behavior, flagging suspicious activities.

2. Data Encryption: Protecting Information in Transit and at Rest

Data encryption is the foundation of healthcare data security, especially for Protected Health Information (PHI). In a zero-trust environment, encryption ensures that even if data is intercepted, it remains unreadable without proper decryption keys.

Cloud services provide:

End-to-End Encryption

Protects data in transit (during transmission) and at rest (when stored).

Key Management Services (KMS)

Securely handles encryption keys for controlled access.

This is critical for safeguarding data shared through telehealth platforms, remote monitoring devices, and cloud-based EHR systems.

3. Automated Compliance Tools: Ensuring Continuous Regulatory Adherence

Healthcare organizations must comply with strict regulations like HIPAA, GDPR, and the HITECH Act. Manual compliance processes can be time-consuming and error-prone, especially in complex IT environments.

Cloud platforms offer automated compliance tools that:

Monitor Data in Real-Time

Quickly identify potential compliance violations.

Automate Risk Assessments

Detect vulnerabilities before they become threats.

Simplify Reporting

Platforms like AWS Compliance Center and Microsoft Compliance Manager provide pre-configured templates for regulatory audits.

This ensures continuous regulatory compliance while reducing administrative overhead.

The Role of Cloud Security in Ensuring Zero Trust in Healthcare

While cloud services provide the infrastructure to implement Zero Trust, cloud security solutions are the mechanisms that ensure it functions effectively. These solutions help healthcare organizations monitor, control, and secure data across complex, multi-cloud environments where traditional security measures often fall short.

1. Cloud Access Security Brokers (CASBs): Enforcing Data Governance Across Cloud Applications

CASBs act as a bridge between healthcare organizations and cloud applications, offering real-time visibility into how data is accessed and shared. They enforce access controls to ensure that only authorized users can interact with sensitive information, detect unauthorized apps (shadow IT), and apply Data Loss Prevention (DLP) policies to prevent accidental or malicious data leaks. CASBs are crucial for securing data in remote work settings and across personal devices, maintaining compliance with healthcare regulations like HIPAA.

2. Encryption and Tokenization: Advanced Data Protection for Multi-Cloud Environments

In a Zero Trust framework, encryption and tokenization provide robust protection for Protected Health Information (PHI). Encryption converts sensitive data into unreadable code, safeguarding it during transmission and storage. Tokenization replaces sensitive data with non-sensitive placeholders (tokens), ensuring that even if data is breached, it remains useless without the corresponding mapping system. Together, these techniques secure data across EHR systems, billing platforms, and multi-cloud environments.

3. AI-Driven Threat Detection: Proactive Security for Real-Time Threat Mitigation

AI-driven threat detection leverages machine learning to analyze large volumes of data, identifying suspicious behavior and potential threats in real time. AI can predict breaches, detect insider threats (e.g., unusual data access patterns), and automate responses like isolating compromised devices or revoking access. This proactive approach significantly reduces response times, helping healthcare organizations contain threats before they escalate.

Future Trends of Zero Trust in Healthcare

As technology evolves, so does the Zero Trust model. Here’s a glimpse into how Zero Trust is expected to shape the future of healthcare cybersecurity:

1. AI-Powered Security: Predictive Threat Analysis for Proactive Defense

The future of Zero Trust lies in predictive security, where AI doesn’t just detect threats—it anticipates them. Machine learning algorithms will analyze historical data to identify patterns that indicate potential vulnerabilities, allowing organizations to address security risks before they are exploited.

This shift from reactive to proactive security will be crucial in healthcare, where the cost of a breach can be measured not just in dollars, but in patient outcomes.

2. Zero Trust for IoT: Securing Medical Devices in Connected Healthcare Environments

The rise of Internet of Medical Things (IoMT) devices—such as smart infusion pumps, remote patient monitors, and connected imaging systems—introduces new vulnerabilities. Many of these devices were not designed with security in mind, making them easy targets for attackers.

Zero Trust principles are being adapted to secure these devices by:

Authenticating every device before allowing network access

Segmenting IoT devices from critical healthcare systems

Continuously monitoring device behavior for anomalies

This ensures that even if one device is compromised, it cannot be used as a gateway to access the broader network.

3. Decentralized Identity Verification: Leveraging Blockchain for Secure Access Management

Blockchain technology is poised to revolutionize identity management in healthcare. By creating decentralized, tamper-proof identity records, blockchain can eliminate the risks associated with traditional, centralized identity systems.

In a Zero Trust model, blockchain enables:

Secure, verifiable digital identities for patients, clinicians, and devices

Immutable audit trails for compliance and security investigations

Reduced reliance on third-party identity providers, minimizing potential attack vectors

This approach aligns perfectly with Zero Trust’s goal of ensuring that every access request is verified, authenticated, and authorized—without relying on implicit trust.

Conclusion: The Future of Healthcare Security is Zero Trust

As healthcare organizations face growing threats from ransomware, data breaches, and insider threats, the need for a proactive, resilient security model has never been greater. Zero Trust, supported by cloud services and AI-driven security solutions, offers a comprehensive framework to protect sensitive data, ensure regulatory compliance, and maintain patient trust.

Ready to modernize your healthcare security with Zero Trust? Contact us today to learn how we can help your organization implement a secure, scalable, and compliant Zero Trust architecture tailored to your unique needs. 

Author: Venkat Bonam
Director – Global Delivery
Driving innovation and leading high-performance teams to create impactful digital solutions that drive business growth and exceed customer expectations.

Introduction

In 2019, a widely used AI system designed to help hospitals allocate healthcare resources was exposed for favoring white patients over black patients, despite both having the same level of medical need. The bias wasn’t intentional—rather, it stemmed from flawed training data that used historical healthcare spending as a proxy for patient needs. Since Black patients had historically less access to healthcare, their spending was lower, causing the AI to deprioritize their care. This wasn’t just an ethical failure—it was a systemic risk that reinforced existing health disparities.

This case isn’t an anomaly. AI models across diagnostics, treatment recommendations, and administrative decision-making are prone to bias, lack of transparency, and privacy risks. Without ethical safeguards, AI can exacerbate inequalities rather than eliminate them. 

This guide explores why ethical AI matters, the risks of neglecting it, and a practical framework to ensure AI is trustworthy, fair, and patient-centered. The future of healthcare AI isn’t just about innovation—it’s about ensuring that innovation serves all patients equitably.

Key Takeaways:

​​Healthcare AI systems can perpetuate or worsen health disparities when implemented without ethical safeguards. A 2019 case study revealed AI bias in resource allocation favoring white patients due to historical spending data.

Data privacy breaches in healthcare are escalating dramatically, with a 192% increase in 2023 and affecting 82% of the US population by 2024, highlighting the critical need for robust security measures.

Successful ethical AI implementation requires a multi-stakeholder approach. The Mayo Clinic demonstrated this with their explainable AI (XAI) in radiology, improving both diagnostic accuracy and physician trust.

Organizations must measure ethical AI performance through specific KPIs including fairness scores, bias detection rates, and transparency indices. Case studies show AI dermatology tools can be up to 40% less accurate for darker skin tones without proper diversity in training data.

Privacy-first techniques like federated learning and encrypted data processing are becoming essential, not optional, for healthcare AI implementation. This ensures HIPAA and GDPR compliance while maintaining AI effectiveness.

Building trust requires transparent communication with patients about AI use in their care. Healthcare providers should implement clear consent processes and patient education initiatives to improve AI literacy and engagement.

Why Ethical AI Matters in Healthcare

AI has the potential to enhance diagnostic precision, create tailored treatment plans, improve patient financial experiences, and streamline hospital workflows. However, these advancements mean nothing if patients and healthcare providers don’t trust AI to be fair, transparent, and secure.

A lack of transparency in AI-driven decision-making can make it difficult for doctors and patients to understand why specific medical recommendations are made. Moreover, data privacy concerns are on the rise, as AI requires vast amounts of personal health data to function effectively.

This lack of confidence highlights the urgent need for ethical AI frameworks that prioritize fairness, transparency, and accountability. If healthcare organizations fail to address these concerns, AI adoption could face resistance, limiting its ability to improve patient outcomes and reduce healthcare disparities.

Ethical AI as a Competitive Advantage

AI is transforming healthcare, enhancing diagnostic accuracy, optimizing treatment plans, and streamlining operations. However, the difference between ethical and unethical AI can mean the difference between life-saving care and harmful misdiagnoses. A well-designed, ethical AI system can improve patient outcomes and build trust, while a biased or opaque AI model can exacerbate health disparities, misallocate resources, and erode confidence in healthcare technology.

AI Success: Ethical AI in Action

The Mayo Clinic provides a prime example of how ethical AI can improve patient care. They implemented explainable AI (XAI) models in radiology, allowing doctors to see how AI reached its conclusions when analyzing medical images. This transparency helped radiologists validate AI recommendations, detect errors, and confidently incorporate AI into decision-making. The result? Higher diagnostic accuracy, faster detection of abnormalities, and improved patient outcomes. 

The Consequences of Unethical AI

Compare this to an AI-driven dermatology tool that was found to be 40% less accurate for patients with darker skin tones. The failure occurred because the AI model was primarily trained on light-skinned patients, meaning it struggled to detect skin conditions like melanoma in patients with darker skin. This lack of diverse data led to higher rates of misdiagnosis, putting lives at risk. Patients of color were left vulnerable, highlighting why fairness and diversity in AI training data are non-negotiable.

Learn more about the evolution of AI in 2025.

Challenges Organizations Face Without Ethical AI

Implementing AI without a strong ethical framework can lead to several issues:

Informed Consent Issues

Patients may be unaware of how their data is used in AI applications, leading to a lack of informed consent. It's essential for healthcare providers to ensure patients understand and agree to the use of their data in AI systems.

Lack of Transparency

AI systems often operate as "black boxes," making it difficult for healthcare providers and patients to understand how decisions are made. This opacity can lead to mistrust and reluctance to adopt AI-driven solutions.

Algorithmic Bias and Fairness Gaps

If AI models are trained on biased data, they can perpetuate or even exacerbate existing health disparities. For example, an AI system trained predominantly on data from one demographic may not perform well for others, leading to unequal care.

Data Privacy Risks

Poorly secured AI systems are vulnerable to cyberattacks, unauthorized access, and data leaks, putting patients’ sensitive medical histories at risk. Organizations that fail to prioritize strong encryption, data anonymization, and regulatory compliance not only endanger patient privacy but also face hefty legal fines and reputational damage.

Regulatory Non-Compliance Risks

Failing to adhere to ethical standards can result in legal penalties, reputational damage, and loss of patient trust. Regulatory bodies are increasingly scrutinizing AI applications in healthcare to ensure they meet ethical and legal standards.

In 2021, 60 million healthcare records were breached, and 57 million in 2022, but the following year saw a 192% increase to 168 million breached records, followed by a 63.5% increase to 275 million records in 2024. Last year, the records of 82% of the population of the United States were exposed, stolen, or impermissibly disclosed.

Ethical AI in Healthcare Implementation Guide

Implementing ethical AI in healthcare is not just about regulatory compliance—it’s about building trust, ensuring fairness, and prioritizing patient safety. For organizations looking to integrate AI responsibly, the following actionable steps provide a structured approach to governance, transparency, fairness, privacy, and explainability.

1. Build a Strong AI Governance Framework

What to Do:

Establish an AI ethics committee that includes clinicians, ethicists, AI engineers, legal experts, and patient advocates.

Define clear accountability structures to determine who oversees AI decision-making and system outcomes.

Align governance policies with global regulations such as HIPAA, GDPR, and the FDA’s AI guidelines.

Conduct regular AI ethics audits to assess compliance, fairness, and potential risks.

2. Ensure Informed Consent and Data Transparency

What to Do:

Create clear, accessible AI consent forms that explain:

  • Why AI is used in diagnosis or treatment.
  • What patient data is collected and how it is processed.
  • Potential risks and benefits of AI-driven decisions.

Implement patient education initiatives to improve AI literacy and encourage informed decision-making.

Develop real-time patient data access portals, allowing individuals to track how their health data is used in AI models.

3. Mitigate Bias Through Fair AI Training

What to Do:

Use diverse, representative datasets that include patients from different ethnic, gender, and socioeconomic backgrounds.

Implement bias detection tools like IBM AI Fairness 360 or Google’s What-If Tool to identify and correct disparities.

Conduct fairness audits before deploying AI in real-world clinical settings.

Train AI models on inclusive medical literature and datasets to reduce diagnostic disparities.

4. Strengthen Data Privacy and Security

What to Do:

Adopt privacy-first AI techniques, such as federated learning, to process patient data without centralizing sensitive information.

Encrypt all patient data and implement multi-layer access controls to protect against breaches.

Conduct regular cybersecurity audits to assess vulnerabilities in AI-driven systems.

Comply with HIPAA, GDPR, and local data protection laws to ensure responsible AI deployment.

5. Prioritize Explainability and Transparency

What to Do:

Develop explainable AI (XAI) models that provide clear insights into AI-driven decisions.

Train healthcare professionals on how to interpret AI-generated recommendations before integrating them into clinical workflows.

Use visual dashboards and interactive tools to make AI predictions more transparent to both clinicians and patients.

Encourage human oversight, ensuring AI assists, rather than replaces, medical decision-making.

How to Measure Ethical AI Implementation: Key Performance Indicators (KPIs)

Organizations must establish measurable KPIs to monitor fairness, transparency, and accountability to ensure AI systems remain ethical.

Fairness Score

Measures whether AI models provide equitable treatment across different demographic groups.

 

How to Track It:

  • Conduct demographic performance audits on AI predictions.
  • Use bias detection tools like Google’s What-If Tool or IBM AI Fairness 360.
  • Compare AI-generated diagnoses against historical patient outcomes.

Bias Detection Rate

Tracks how frequently AI requires bias-related adjustments post-deployment.

 

How to Track It:

  • Run bias audits every 6-12 months to detect shifts in AI accuracy across patient groups.
  • Compare misdiagnosis rates before and after AI interventions.
  • Implement real-time AI fairness monitoring dashboards.

Transparency Index

Assesses the explainability of AI outputs for medical professionals and patients.

 

How to Track It:

  • Survey clinicians on AI interpretability in decision-making.
  • Assess how often doctors override AI-generated recommendations due to lack of clarity.
  • Deploy Explainable AI (XAI) tools that provide decision rationales in medical reports.

Patient Consent Compliance Rate

Evaluates whether AI systems obtain proper informed consent before processing sensitive data.

 

How to Track It:

  • Track the percentage of AI-powered treatments or diagnostics that include patient-approved consent forms.
  • Implement digital consent platforms that allow patients to review and approve AI interactions.
  • Conduct annual compliance audits to ensure adherence to HIPAA, GDPR, and local regulations.

Data Privacy Compliance Score

 Ensures AI adheres to HIPAA, GDPR, and other relevant healthcare regulations.

 

How to Track It:

  • Conduct quarterly cybersecurity audits to detect vulnerabilities.
  • Measure compliance with data encryption, anonymization, and access control policies.
  • Monitor incident reports on unauthorized AI-driven data access.

Human-AI Collaboration Metrics

Examines the impact of AI on clinical decision-making and patient outcomes.

 

How to Track It:

  • Analyze whether AI recommendations align with or enhance clinician decisions.
  • Measure time saved per diagnosis and reduction in misdiagnosis rates.
  • Track physician adoption rates of AI-assisted tools in clinical workflows.

Healthcare organizations can ensure that their AI technologies align with long-term ethical standards by establishing real-time monitoring systems and conducting routine AI ethics audits.

Conclusion

As AI adoption in healthcare accelerates, organizations must ensure ethical implementation is at the core of their strategy. How is your institution addressing ethical AI? Start by auditing your current AI models for bias, transparency, and compliance today.

Get access to our latest whitepaper on AI Paradox in healthcare

Author: Karthick Viswanathan
Director ATG & AI Practice
Technology leader with 20+ years of expertise in generative AI, SaaS, and product management. As Executive Director at Amzur Technologies, he drives innovation in AI, low-code platforms, and enterprise digital transformation.

Healthcare Archives - Amzur Technologies

Services Offered: Application Development

Industry: Healthcare

Introduction

Amzur developed an automated and comprehensive healthcare management solution that brings patients, healthcare takers, and insurance providers to the same page without any communication gaps and errors. Amzur’s turnkey solutions make disparate departments work together by embracing modern technology and leveraging data and analytics. Our healthcare management solutions promise that the health system works better for everyone and helps businesses scale up at a pace.

Client Overview

Medical data processing and claim management are crucial parts of the healthcare industry. Manual processing and errors continue to be the biggest hurdles for insurance companies, hospitals, and other providers.

One of our clients from the healthcare industry wanted to develop an inclusive application to automate the documentation process and make the data available across the organization to minimize manual intervention. They approached Amzur to develop a solution to improve drug administration and free up doctors to spend time with patients.

Challenges

Industries are moving at a fast pace to embrace technology and gain the benefits brought by business modernization. The healthcare industry is no exception. Since the healthcare industry is data-sensitive and often requires human interaction, it can be a challenge to implement and achieve automation and process efficiency.

Our client faced challenges with extensive human intervention, tedious task management, real-time viewing of business processes and data, claim processing (which further complicated data processing), disaster management, and cross-department communication.

Solution

Before developing a comprehensive solution, we identified a few customer challenges including, speed, accuracy, transparency, tedious tasks automation, claim processing, and data security.

After a thorough analysis, our team of experts divided the entire development process into three phases to resolve every challenge with the utmost attention.

The three phases are:

Claims management module

Clinical 360 services

Integrated ancillary services

We identified the need for process automation and developed modern healthcare management and automation solutions. For this application, we built:

A configurable rules engine and workflow engines to replace manual operations.

A process to move repetitive tasks to the business rules engine and workflow engine.

An automated process based on business rules definitions to move erroneous manual tasks to automated tasks.

A 360-degree solution that made it possible to provide a single source of truth for data across the organization for better communication.

Our four-tier architecture focuses on improving efficiency while minimizing human intervention and erroneous operations.

Business Access Layer: Contains business logic, and validation that is related to the data.

Data Access Layer: Contains logic for the business layer to connect to the data and performs required actions like insert, delete and update.

Presentation Layer: Contains .asp or HTML and forms the user interface, gives an output from the system, or takes inputs from the user.

Data Standardization Layer: Evaluate incoming claim data and prepare vital information for the processing engine.

Tech Stack

We are always ahead in embracing modern technologies that can ensure prominent growth and scalability.

Angularjs + C# + MVC + Web API + Kendo UI + HTML 5 + CSS 3

Windows Server 2008 was used with IIS 7 that followed MVC architecture

The benefits of MVC architecture are:

It can be integrated easily with Javascript frameworks

MVC architecture provides total control over the rendered HTML

TDD – provides test-driven development

MVC architecture provides total control over the rendered HTML

Benefits

Amzur’s healthcare management system benefits various sectors including, the government, insurance companies, patients, and hospitals in:

Bringing disparate data together to make well-informed decisions.

Improving drug administration accuracy by 40%.

Freeing up doctors to spend more time with patients.

Error-free claim verification and processing.

Saving more than 60% of employee time from mundane tasks.

Conclusion

Today, business and process automation have become paramount to stay relevant and competitive in the market. Especially in the healthcare industry, it is crucial to ensure everyone including, patients, insurance companies, and doctors is free from monotonous and tedious tasks.

Failing to achieve workflow automation and claim processing wouldn’t yield any positive results and only further hinders growth. Our client came to us with numerous challenges to overcome and ideas to implement. By combining our technical expertise and vast industrial experience, we built a futuristic healthcare management application that enables businesses to access real-time data through customized dashboards and provides insights that contribute to seamless growth.

Client’s feedback

Right from the word go, Amzur delivered beyond the call of action. They have an excellent development team, who are available 24-7 to support us.

We have a process where we give regular feedback on projects, teams, and not a single instance where we rated them below 5/5.

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