EHR CRM Integration and Medical BI Implementation for a Healthcare Network

Our team of BI developers configures healthcare dashboards and reports for your organization by consolidating data from diverse sources. We offer implementation of Amazon QuickSight, Microsoft Power BI, Tableau, Google's Looker, Oracle, SAP, Sisense, and more. What is Business Intelligence in Healthcare? Healthcare business intelligence, as a subset of healthcare data analytics, takes historical health-related data from multiple internal and external sources and visualizes it multidimensionally. EHR/EMRs, labs, eHealth/mHealth apps and smart wearables, governmental agencies, accounting tools, and CRM platforms are among some of them. Data is saved, then analyzed, and finally reported. Cloud database development makes the process of healthcare data storage, data retrieval, and data analysis more efficient and secure. Using the information gained, it's possible to improve patient satisfaction and the financial performance of medical centers, clinics, hospitals, insurance vendors, research facilities, pharmaceutical companies, and data technology firms. Top Features to Look For in Healthcare Business Intelligence Software Security. User administration, platform access auditing, authentication management) Cloud-Readiness. The ability to build, deploy, and manage the BI software in the cloud across multi-cloud and hybrid cloud deployments. Data Source Connectivity. Enabling users to connect to and ingest data from various storage platforms, including on-premises and cloud. Supporting users to combine data from different sources using drag-and-drop. Data Preparation. Creating analytic models with user-defined measures, sets, groups, and hierarchies. Automated Insights, Natural Language Generation, and Data Storytelling. Applying machine learning to automatically generate insights and identify the most important attributes in a dataset. Automatically creating descriptions of insights in data that explain key findings or the meaning of charts or dashboards. Generating news-style data stories that combine headlines, narrative text, data visualizations, and audiovisual content based on ongoing monitoring of findings. Natural Language Searching. Enabling users to query data using terms typed into a search box or spoken. Data visualization. Supporting highly interactive dashboards and exploring data through manipulating chart images, including heat maps, tree maps, geographic maps, scatter plots, and other special-purpose visuals. Reporting. Providing parameterized, paginated, and pixel-perfect reports that can be scheduled and burst to a large user community. Top 7 Business Intelligence Software Tools for Healthcare With an emphasis on visual self-service, today's healthcare BI software incorporates AI and empower non-technical users to model and analyze data and share insights. Gartner lists Amazon QuickSight, Microsoft Power BI, Tableau, Google's Looker, Oracle, SAP and Sisense among top BI software providers. What possibilities do they bring to health-related companies? #1 Amazon QuickSight The key feature of Amazon's business intelligence tool, QuickSight, is a generative AI assistant named Q. It creates interactive visualizations, dashboards, reports, and customizable data stories on demand—without sending requests to the busy and overloaded BI team or waiting weeks or even months—simply by typing exact questions into the Q bar. Outputs include citations and references for transparency. API access allows integration of this capability into third-party applications. To make this business intelligence tool work, it should have access to your documents, images, files, and other application data, as well as structured data stored in databases and data warehouses. QuickSight connects with over 50 commonly used business tools and unstructured data sources (wikis, intranets, Atlassian, Gmail, Microsoft Exchange, Salesforce, ServiceNow, Slack, etc.). Get help with Implementing Amazon QuickSight #2 Microsoft Power BI Microsoft Power BI is a comprehensive data analytics tool available as a software-as-a-service option on Azure. It provides data preparation, visual data exploration, interactive dashboards, and augmented analytics. Power BI Premium includes AI-powered text, sentiment, and image analytics. Power BI seamlessly integrates with Office 365, including Microsoft Teams, Excel, and SharePoint. It can be enhanced by embedding Power Apps into its dashboards, and Power Automate flows can automate tasks based on the data. However, Power BI is limited to deployment on Azure and does not offer options for other cloud infrastructure as a service (IaaS). While data connectivity enables multi-cloud and hybrid cloud scenarios, governance of self-service usage is a common concern. On-premises Power BI Report Server has a more limited offering without features such as dashboards, streaming analytics, prebuilt content, natural language question and answer, automated insights, and alerting. To overcome the limitations of Power BI and use a more integrated analytics experience, as well as fully utilize their data infrastructure, organizations can transition to Microsoft Fabric. Belitsoft offers expert migration services to facilitate this shift, making the transition effortless for your workflows. Get help with Implementing Power BI #3 Tableau Tableau, a product from Salesforce, offers a user-friendly way to access, prepare, analyze, and present data. It empowers business users to explore visually their data with an intuitive drag-and-drop interface powered by the VizQL engine. Tableau provides a natural language query feature called Ask Data that can be integrated into a dashboard, and a data explanation tool called Explain Data. The vendor focuses on extending their natural language generation and data storytelling capabilities. Analysts can curate existing datasets using Lenses and access dashboard accelerators on the Tableau Exchange. The tool also offers centralized row-level security and virtual data connections. However, Tableau's licensing costs are relatively high, with additional fees required for features such as Data Management, Server Management, and Einstein Discovery. Some users report below-average satisfaction with Tableau's overall service and support, making it sometimes challenging to find Tableau-specific assistance. Get help with Implementing Tableau #4 Google’s BI software for healthcare Google's Looker is a cloud-based BI platform that provides users with self-service visualization and dashboard capabilities. It supports multi-cloud scenarios for deployment and database connectivity, with continuous integrations with other Google Cloud products like BigQuery. Looker's extension framework is a fully hosted development surface that allows developers to build data-driven applications. It offers direct query access to cloud databases, lakes, and applications as its primary data connectivity method. This enables users to leverage LookML's virtualized semantic layer without having to move their data. Google aims to open up the LookML data modeling layer to other BI platforms, including Microsoft Power BI, Tableau, and its own assets like Data Studio, Google Sheets, and Google Slides. Looker's APIs, software development kits, and extension framework, including the Data Dictionary, enable customers to create customer-facing applications and embed analytics in business workflows. The Looker Marketplace offers prebuilt data and machine-learning model Blocks to address common analytical patterns and sources. While Looker may have coding requirements compared to competitors' drag-and-drop data modeling and advanced analytics capabilities, it provides prebuilt data and ML model Blocks to mitigate this. However, Looker currently lacks augmented analytics features for automated insights, data storytelling, and Natural Language Generation, and its Natural Language Query interface is weaker compared to competitors. Get help with implementing Google's Business Intelligence software #5 Oracle Healthcare BI Oracle offers a comprehensive BI cloud solution that includes infrastructure, data management, and analytics applications. With data centers in 30 regions, Oracle supports customers' multicloud needs through an open architecture approach. Oracle focuses on conversational user experiences and automated data storytelling features. These include generating audio podcasts that highlight key trends, data changes, outliers, and contextualized insights. Users can benefit from Natural language queries in 28 languages and Oracle Analytics Day by Day for mobile devices. For on-premises deployments, Oracle offers Oracle Analytics Server, and for Oracle Cloud Applications, prebuilt analytics solutions are available through Fusion Analytics Warehouse. The Oracle warehouse provides native integration for Oracle's ERP, human capital management, supply chain, and NetSuite products. Although Oracle Analytics Cloud can access any data source, its packaged analytic applications (Fusion Analytics Warehouse and NetSuite Analytics Warehouse) are designed specifically for Oracle enterprise applications. Non-Oracle application customers would need to build their own applications using Oracle Analytics Cloud to gain similar capabilities. It's worth noting that customers have reported below-average satisfaction with Oracle's service and support. Additionally, the legacy Oracle Healthcare Foundation (OHF) analytics solution is no longer actively supported. Get help with implementing Oracle Healthcare Business Intelligence software #6 SAP Healthcare BI SAP Analytics Cloud is a cloud-based platform that integrates with SAP cloud applications and can query both cloud and on-premises SAP resources, such as SAP Business Warehouse, for live data. Its user-friendly Story Viewer and Story Designer tools enable non-technical users to create and interact with dashboards and reports. The Analytics Designer, a low-code development environment, facilitates the creation of analytics applications using APIs. SAP Analytics Cloud stands out with its integrated functionality for planning, analysis, and prediction. It offers "what-if" analysis, change tracking, and calculation capabilities. The platform also includes strong functionality for natural language generation, natural language processing, and automated insights. Its integrated functionality for planning, analysis, and prediction sets it apart from other platforms. For the healthcare industry and related lines of business, SAP Analytics Cloud provides pre-built business content, including data models, data stories, and visualizations. However, it is primarily utilized by existing SAP business application customers and legacy business intelligence users. Customers without a SAP-centric application or data ecosystem typically do not opt for SAP Analytics Cloud. While SAP Analytics Cloud is a cloud-native platform that can query on-premises data, customers seeking an on-premises deployment would need to use a standalone SAP BusinessObjects BI to fully leverage the analytics catalog functionality and Universe connector for a complete hybrid deployment experience. Get help with implementing SAP Healthcare Business Intelligence software #7 Sisense Healthcare BI Sisense is a self-service analytics platform that offers advanced analytics and application development capabilities. Many users utilize Sisense in its OEM form. Sisense Fusion focuses on integrating analytics into business workflows, providing interactive visualizations and natural language query capabilities. It offers a microservices-based architecture that is fully extensible, allowing for embedding analytics into applications and workflows. Sisense Notebooks serve as a bridge between data professionals and self-service users who want to perform advanced analysis using SQL, Python, R, and other programming languages. Infusion Apps provide users with prebuilt examples for Google Chrome, Google Sheets, Google Slides, Microsoft Teams, Salesforce, and Slack, helping to tie analytics to actions. Sisense Fusion is cloud-agnostic and multicloud-capable, with deep partnerships with AWS, Google Cloud, and Microsoft, as well as strong cross-cloud analytics orchestration. Sisense's analytics marketplace is a one-stop shop for publishing and building analytics artifacts, including connectors, applications, and workflows. Sisense can catalog other analytic vendors' assets via APIs, and it offers extensible connectivity to other reporting tools. Developers can utilize the Extense Framework to create custom applications or workflows or choose from prebuilt Infusion Apps for embedding analytic capabilities. However, customers have reported below-average evaluations of third-party resources, such as integrators and service providers, as well as the overall quality of the peer user community. Sisense's service and technical support have also received below-average evaluations. Get help with implementing Sisense Healthcare Business Intelligence software We work with B2B healthtech companies to help their clients make better use of healthcare information. Our developers create custom healthcare software based on their requests. Shortlist our company as your potential partner that has an available pool of talented data analysts and BI consultants for healthcare who can solve any business intelligence challenge by developing, customizing, and implementing complex analytics solutions. Benefits of Business Intelligence and analytics for healthcare organizations BI Consolidates Health Data and Protects It Business intelligence in healthcare is about consolidating clinical, administrative, and financial data. It works even with previously loosely-related systems. But it goes beyond that. Business intelligence tools allow one to protect sensitive patient information. Access to different parts of this data is easily restricted to comply with HIPAA law and more. BI Improves Decision-Making Business intelligence is a holistic visualization of all the KPIs you're tracking. It connects to multiple data sources to put the information into a single, centralized repository - a data warehouse. BI reports and dashboards answer the question "What happened?", and "Why did it happen?" can be explored with drill-down analysis. BI predictive analytics is based on data scientists' calculations. It's often more justified than personal opinions. Machine learning and statistics are unbiased ways to understand "What can we expect as a result?" Simulation and scenario analysis make clear "What actions should we take?" BI Reduces Healthcare Costs Business intelligence can quickly interpret large and complicated data like bills, medical records, and financial statements and provide useful information in a few hours instead of days. Coming from the research on clinical activities, supplies, logistics, costs, and outcomes, a BI helps turn data into timely resolutions. It links and puts together huge amounts of data from providers, life sciences organizations, and insurers to find cost savings, trends, and optimal treatments and medications. With quick situational insights, unexpected challenges can be mitigated, and resources can be used more efficiently. By leveraging built-in AI capabilities, it is possible to predict and plan for future needs. Avoid Costly Readmissions BI software highlights the patients with a certain condition who are readmitted within, for example, 30 days of discharge. It determines the factors contributing to these readmissions, for example, medication non-adherence. Steps to address them may involve providing patients with better education and support to ensure they take their medication correctly or improving follow-up care after discharge. Prevent Chronic Patients From Complications Business Intelligence systems identify the patients with a certain condition are at risk for complications, like foot ulcers or kidney disease. Taking action on these cases in the initial stages leads to more targeted interventions and prevents high expenditures on developed complications. It may concern mostly medication management, acting as reminders for drug refills or pill organizers to help patients stay on track with their treatment regimen. Or it aligns remote monitoring programs that include wearable devices to track blood glucose or blood pressure levels and send alerts to healthcare providers if the levels are outside of the target range. Optimize Healthcare Supply Chain Management In the healthcare sector, supply costs are considerably high. However, leveraging data analysis BI tools holds great potential to bring down these costs. With healthcare supply chain analytics, you can identify and forecast variations in demand or potential supply disturbances, quickly recognize and address supply chain problems, and prevent or ease shortages of medical supplies and drugs. Through monitoring inventory levels and expiration dates, then evaluating usage patterns, it minimizes waste by pinpointing areas where overstocking is taking place and adjusting inventory levels accordingly. Improved Patient Treatment Building a data-driven approach in healthcare propels this domain forward, as 94% of healthcare stakeholders believe. They emphasized the top advantage doctors and patients can leverage from implementing healthcare BI tools and data analytics: a more personalized treatment path. Dmitry Baraishuk Chief Innovation Officer at Belitsoft on Forbes.com Predicting Surgical Complications Healthcare BI tools with predictive analytics can determine a patient's risk of post-surgical complications, such as kidney failure and stroke. It should develop a special model collaboration with a multidisciplinary team comprising a surgeon, cardiologists, nephrologists, and other specialists. This predictive model determines which patients were likely to suffer a stroke, cardiac event, or die within 30 days of surgery. Health-related providers can use it at a patient's bedside to conduct pre-surgery assessments. Clinicians inform surgeons of potential risks and better advise patients, resulting in improved care delivery. Identify Patterns and Trends in Patient Health Outcomes The organization uses BI tools to analyze data from electronic health records: patient demographics, medical history, and treatment outcomes. Healthcare providers commit a notice. For instance, the patients with a particular condition are experiencing longer hospital stays and higher rates of readmission compared to patients with the same case at other hospitals. The Business Intelligence team works with the hospital staff to examine potential causes, like delays in diagnostic testing, longer wait times for specialty consultations, and slower medication reconciliation processes. After they operate the data to implement targeted interventions, such as optimizing the order of diagnostic tests, reducing wait times for specialty consultations, and streamlining the medication reconciliation process. Because of this interference, the hospital improves better patient outcomes. Limitations of Healthcare Business Intelligence Data entry, management, interpretation, and sharing can often rely on manual processes, which are prone to errors, particularly in the healthcare industry. Without a coherent system of accountability in place, these errors can accumulate and lead to further complications. Healthcare data is a complex and heterogeneous collection originating from various sources and takes many forms. This includes patient profiles, healthcare provider information, pharmaceutical company data, disease registries, diagnostic tests, treatment options, and various types of visual data, such as scans, images, and graphs. The above databases are constantly growing as new admission, diagnostic, treatment, and they add medical records on discharge. The diverse nature of these data sources presents significant challenges with aggregating and integrating the data, constructing a data warehouse, and loading the data into a rules-based engine for generating actionable insights and reports. Reliable Health Business Intelligence depends on accurate data access. Thus, prior to introducing a BI solution, it is vital to configure robust data management. Healthcare Business Intelligence Analyst A skilled BI analyst is essential, especially during the initial configuration of healthcare BI software and self-service tools. Their primary responsibility is to customize data models and dashboards to align with the unique needs of a health-related organization. Business Intelligence Analysts work with company data to identify areas for improvement in current processes and establish metrics or KPIs to track product performance and identify areas of improvement. These analysts possess strong data visualization skills to present their findings in a clear and understandable format to stakeholders. The role of a Business Intelligence Analyst extends beyond reporting. They assist businesses in uncovering insights by asking the right questions and exploring data. BI analysts help to guide organizations to discover new knowledge and find answers to unanticipated questions. To achieve this, BI specialists use a range of tools, including web analytics tools, database tools, ETL tools, and full-stack BI platforms like Power BI or Tableau. Requirements often include: Experience in health informatics and healthcare analytics Ability to analyze data and communicate insights through dashboards and reports Strong SQL programming and advanced data manipulation skills Experience building data products using business intelligence software Familiarity with healthcare data sources, such as claims, electronic health records, and patient-reported data Detail-oriented with a focus on producing accurate and polished work Excellent written and oral communication skills The specific responsibilities of a BI analyst vary depending on the company's needs. Example 1: Devoted Health was seeking a Sales Operations BI Analyst who could work with complex data, communicate insights through data visualization, and prioritize data governance. The ideal candidate would collaborate closely with various teams within the company, including business, data science, product management, analytics engineering, data engineering, and software engineering. Example 2: McLaren Health Care network was in search of a BI analyst to handle healthcare claims and quality data reporting, analytics, and statistical analysis. The ideal candidate would have a strong understanding of healthcare data, including cost of care and patient utilization metrics. Experience in healthcare analysis, including statistical methods, data mining, forecasting, simulation, and predictive modeling, was also required. Example 3: Aledade sought a Business Intelligence Data Analyst to provide continuous analytical support, using operational and clinical data to address pressing business questions, support data operations, and project management functions. This role would be a part of the Business Intelligence team. In each case, the analyst's responsibilities varied, such as: collecting and integrating health plan and internal systems data creating data visualization solutions examining trends, providing actionable insights, and supporting stakeholders with operational and clinical data analysis Other key responsibilities of the Data Analyst included: Developing actionable roadmaps for workflows and processes Setting up and organizing KPIs and timelines for deliverables aligned with team objectives Building interactive dashboards, reports, and data visualizations to effectively communicate insights from data and drive action Assisting in the design and implementation of data warehouse tables or views to support analysis and reporting Supporting the team in research, data analysis, meeting preparation, follow-through, and the development of strategies to address health disparities Proactively identifying and flagging major risks or challenges to draw attention, allocate resources, or implement mitigation steps Example 4: Franciscan Health was seeking a Healthcare Business Data Analyst with the following functions: Identifying and proposing evaluation strategies for key performance indicators (KPIs), quality metrics, outcomes, population management studies, and other relevant areas Developing technical and functional specifications based on business requirements and workflow analysis Managing database processing functions, such as merge/purge, data hygiene, data appends, and coordination with business partners Identifying and addressing data quality issues that may affect reporting, such as duplicate records or missing data Utilizing appropriate programming languages and technologies to extract and process data for business analytics Identifying effective methods of data visualization and presentation to communicate project findings to management Tracking and analyzing trends and relevant measures to maximize database capabilities Integrating add-on programs to optimize back-end processes Acting as a liaison between the analytical needs of departments and business partners Business Intelligence Dashboards for Healthcare Healthcare dashboards allow healthcare organizations, including providers and payers, to gain deeper insights into their data by drilling into trends and Key Performance Indicators (KPIs) related to patients, providers, operational departments, clinical records, and finance. A healthcare dashboard offers users a real-time graphical display of their healthcare KPIs. It enables medical institutions to measure and compare metrics, such as patient satisfaction, physician allocation, Emergency Department Wait Times, and occupied bed count. This tool aids in improving operational efficiency, resulting in better outcomes and more intelligent decisions. Executive KPI Dashboard Many measures are now publicly reported, many of which are directly linked to reimbursement and are critical. It's challenging to prioritize what to work on next and respond to constantly changing needs while having fixed resources to improve patient experience, reduce the cost of care, and improve population health. The Executive KPI Dashboard quickly displays critical KPIs. It is vital to understand the performance clearly and focus the efforts on where it's possible to maximize returns. This dashboard accelerates information sharing and provides a scaffolding to automate the collection of critical data elements and unify analytics across multiple platforms. The Executive KPI Dashboard accomplishes this by using a consistent, simple, and easy-to-understand visualization of the most critical measures. A quick glance at the dashboard shows the state of dozens of KPIs, including the number on each bar, performance against the benchmark, trend over time, and most recent performance. Users can drill down to a linked dashboard to learn more or access reference material, such as an internal wiki page. Additionally, users can view performance through a statistical process control chart, with signals for particular cause variations automatically highlighted. Executive KPI Dashboard. Tableau Hospital Performance Dashboards The department can monitor a hospital's admissions, comparing the number of doctors and average wait time. Such monitoring can facilitate determining the necessary resources required to run each department. Additionally, tracking patient satisfaction provides a means to measure both the performance of doctors and the overall quality of each division. Establishing a relationship between the user and the dimension allows control over which divisions are visible to which users due to security reasons. Hospital Performance Dashboards. Sisense Dashboards for Patient No-Show Data Analysis and Prediction One common issue in outpatient practices is patient no-shows and late cancellations, which lead to decreased revenue for the practice, and longer wait times for other patients. Our aim is to increase patient attendance and reduce last-minute cancellations, to make more patients being seen by healthcare providers. We could use analytics to predict when patients may not show up or cancel at the last minute, allowing us to take a proactive approach to reduce these occurrences. To achieve this goal, we need to identify the breakdown of appointments by various patient characteristics, and then predict which patients are more likely to cancel, and schedule appointments accordingly. As a simple prevention measure, we can also implement tailored appointment reminders. Additionally, using run charts can provide valuable information about attendance trends and fluctuations over time, helping to further refine our predictive models and intervention strategies. Insurance Claims Dashboards To maintain profitability, insurance companies must continuously monitor the claims made under their various policies. This allows them to modify premiums for policies with chief claims ratios or introduce new policies to reduce premiums for their clients. Additionally, identifying the number of claims per customer or policy can help insurers offer cost-effective premiums that benefit both the customers and the company. The insurance analytics dashboard plays a critical role in achieving these objectives. Hire healthcare BI analyst Get Help with Implementing Business Intelligence Software

What is HIPAA-compliant Database?  A database is an organized collection of structured information controlled by a database management system. To be HIPAA-compliant, the database must follow administrative, physical, and technical safeguards of the HIPAA Security Rule. Often it means limiting access to PHI, as well as safely processing, transmitting, receiving, and encrypting data, plus having a proactively breach mitigation strategy. Administrative, physical, and technical safeguards of the HIPAA Security Rule HIPAA Rules for Database Security If your database contains even a part of PHI, it is covered by the HIPAA Act of 1996 and can attract the attention of auditors. PHI is the information containing any identifiers that link an individual to their health status, the healthcare services they have received, or their payment for healthcare services. The HIPAA Security Rule (the part of HIPAA Act) specifically focuses on protecting electronic PHI. Technical safeguards (the part of HIPAA Security Rule) contain requirements for creating a HIPAA-compliant database. Centers for Medicare & Medicaid Services (CMS) covers HIPAA Technical Safeguards for database security in their guidance. The first question that can arise is whether you should use any specific database management system to address the requirements? The answer is absolutely no. The Security Rule is based on the concept of technology neutrality. Therefore, no specific requirements for types of technology are identified. Businesses can determine themselves which technologies are reasonable and appropriate to use. There are many technical security tools, products, and solutions that a company may select. However, the guidance warns that despite the fact that some solutions may be costly, it can’t be the cause of not implementing security measures. "Required" (R) specifications are mandatory measures. "Addressable" (A) specifications may not be implemented if neither the standard measure nor any reasonable alternatives are deemed appropriate (this decision must be well-documented and justified based on the risk assessment). Here are the mandatory and addressable requirements for a HIPAA-compliant database. Mandatory HIPAA Database Security Requirements HIPAA Compliant Database Access Control Database authentication. Verify that a person looking for access to ePHI is the one claimed. Database authorization. Restrict access to PHI according to different roles ensuring that no data or information is made available or disclosed to unauthorized persons. Encrypted PHI PHI must be encrypted both when it is being stored and during transit to ensure that a malicious party cannot access information directly. Unique User IDs You need to distinguish one individual user from another followed by the ability to trace activities performed by each individual within the ePHI database.  Database security logging and monitoring All usage queries and access to PHI must be logged and saved in a separate infrastructure to archive for at least six years.  Database backups Must be created, tested, and securely stored in a separate infrastructure, as well as properly encrypted.  Patching and updating database management software Regular software upgrades, as soon as they are available, to ensure that it’s running the latest tech. ePHI disposal capability Methods of deleting ePHI by trained specialists without the ability to recover it should be implemented. By following the above requirements you create a HIPAA-compliant database. However, it’s not enough. All HIPAA-compliant databases must be settled in a high-security infrastructure (for example, cloud hosting) that itself should be fully HIPAA-compliant. HIPAA-Compliant Database Hosting You need HIPAA-compliant hosting if you want either to store ePHI databases using services of hosting providers, or/and to provide access to such databases from the outside of your organization. Organizations can use cloud services to store or process ePHI, according to U.S. Department of Health & Human Services. HIPAA compliant or HIPAA compliance supported? Most of the time, cloud hosting providers are not HIPAA compliant by default but support HIPAA compliance, which means incorporating all the necessary safeguards to ensure HIPAA requirements can be satisfied. If healthcare business wants to start collaborating with a cloud hosting provider, they have to enter into a contract called a Business Associate Agreement (BAA) to enable a shared security responsibility model, which means that the hosting provider takes some HIPAA responsibility, but not all.  deloitte.com/content/dam/Deloitte/us/Documents/risk/us-hipaa-compliance-in-the-aws-cloud.pdf In other words, it is possible to utilize HIPAA compliance supported services and not be HIPAA compliant. Vendors provide tools to implement HIPAA requirements, but organizations must ensure that they have properly set up technical controls - it's their responsibility only. Cloud misconfigurations can cause an organization to be non-compliant with HIPAA. So, healthcare organizations must: be ensured that the ePHI is encrypted during transit, in use, and at rest; enable data backup and disaster recovery plan to create and maintain retrievable exact copies of ePHI, including secure authorization and authentication  even during times where emergency access to ePHI is needed; implement authentication and authorization mechanisms to protect ePHI from being altered or destroyed in an unauthorized manner as well as include procedures for creating, changing, and safeguarding passwords; implement procedures to monitor log-in attempts and report discrepancies; conduct assessments of potential risks and vulnerabilities to the confidentiality, integrity, and availability of ePHI; include auditing capabilities for their database applications so that security specialists can analyze activity logs to discover what data was accessed, who had access, from what IP address, etc. In other words, one needs to track, log, and store data in special locations for extended periods of time. PaaS/DBaaS vs IaaS Database Hosting Solutions Healthcare organizations may use their own on-premise HIPAA-compliant database management solutions or utilize cloud hosting services (sometimes with managed database services) offered by external hosting providers.  Selecting between different hosting options is often selecting between PaaS/DBaaS and IaaS.  For example, Amazon Web Services (AWS) provides Amazon Relational Database Services (Amazon RDS) that not only gives you access to already cloud-deployed MySQL, MariaDB, PostgreSQL, Oracle, Microsoft SQL Server or Amazon Aurora relational database management software, but also removes almost all administration tasks (so-called PaaS/DBaaS solution). In turn, Amazon's Elastic Compute Cloud (Amazon EC2) services are for those who want to control as much as possible with their database management in the cloud (so-called IaaS solution).  on-Premise vs PaaS/DBaaS vs IaaS Database Hosting Solution PaaS/DBaaS vs IaaS Database Hosting Solution Azure also provides relational database services that are the equivalent of Amazon RDS: Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL, and Azure Database for MariaDB. Other database engines such as SQL Server, Oracle, and MySQL can be deployed using Azure VM Instances (Amazon EC2 equivalent in Azure). Our company is specializing in database development and creates databases for large and smaller amounts of data storage. Belitsoft’s experts will help you prepare a high-level cloud development and cloud migration plan and then perform smooth and professional migration of legacy infrastructure to Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. We also employ experts in delivering easy to manage HIPAA-compliant solutions and technology services for medical businesses of all sizes. Contact us if you would like to get a HIPAA risk assessment and analysis.

When thinking about solutions to manage, authenticate, and authorize their users, SaaS owners with the help of SaaS developers have to make architecture design choices. The right design depends on what customers of SaaS platforms need. Using a centralized identity provider and implementing role-based access control seems today the most common, however not exclusive, scenario for identity and access control management. Centralization The approach to implementing the identification and access control by the code in multiple sections within the application is insecure and considered legacy because the app developers can make accidental changes to it. A possible security breach is cross-tenant data access. The modern approach focuses on offloading the authentification/authorization logic to a separate centralized system from the application code. It increases the security of a SaaS application and makes it less vulnerable to attacks or exploitations. Additionally, centralization helps escape the need to write repetitive code each time. Role-Based Access Control Role-based access control is an authorization system that determines access to resources based on a user’s role. It’s a simple access control model to implement because it aligns well with easily recognizable business logic. In such a design, administration permissions can be provided to any level of the group hierarchy. visual-guard.com Example of the Role-Based Access Control for a Hospitality SaaS Solution to protect cloud tenants’ privacy and improve the efficiency of cloud management. This model works well with large-scale users and can cope with the surge of user numbers within limited cloud resources. Example of SaaS Identity and Access Control Management using Azure Active Directory  The Challenge of Multi-Tenant SaaS Identity and Access Control Management and its Solution Let's say you're writing an enterprise multi-tenant cloud-based SaaS application.  The application has two users alice@contoso and bob@fabricam which belong to different organizations. When Alice signs in, the application has to know that Alice employee is part of Contoso Customer and should have access to Contoso Customer data but shouldn't have access to Fabrikam Customer data.  Moreover, each SaaS customer should have the ability to assign the roles like "Admin" or "Standard User" themselves without asking the help from you as the SaaS provider The properly configured processes of verifying the identity of users (Authentication) and controlling their access to resources and actions (Authorization) are solutions to the above-described challenge.  microsoft.com After implementing Azure AD, the flow looks like this: Authentication. Alice from Contoso uses the browser to navigate to the SaaS application and press the "Log in" button. She's redirected to a sign-in screen where she enters her corporate credentials (for example, username and password). She's logged into the app. Authorization. The multi-tenant SaaS application knows that Alice is an admin user for this application and can use the resources that belong to Contoso. However, she can't view Fabrikam's resources, because she's an admin only within her tenant. Architecture for Tenant Authentication Let's say such a multitenant SaaS application consists of a web front-end and a web API backend implemented using ASP.NET Core, which has built-in middleware for the OpenID Connect protocol.  microsoft.com microsoft.com The SaaS web application:  uses the OpenID Connect protocol to authenticate users with Azure Active Directory; calls Azure AD to get OAuth 2 access tokens for the Web API;  cache them in Azure Cache for Redis to enable multiple instances to share the same token (if necessary). What happens when the user signs in, at a high level: The tenant’s user clicks the "sign in" button in the multi-tenant SaaS app. This action is handled by an MVC controller. The MVC controller returns an action to verify their identity. The middleware creates a 302 response, which redirects the user to the Azure AD sign-in page. The user authenticates with Azure AD. Azure AD sends an ID token to the application. The middleware validates the ID token and authenticates the user inside the SaaS application. The middleware redirects the user back to the SaaS application. When the user first signs in, the Cookie Authentication middleware writes the user claims to a session cookie, which gets deleted once the user closes the browser (it’s convenient for banking applications, for example, and can be reconfigured to persistent cookies). After that, HTTP requests are authenticated by reading the cookie. Example of SaaS Identity and Access Control Management using Auth0 and Amazon API Gateway  Basic multi-tenant setup with Auth0 and Amazon API Gateway. aws.amazon.com Auth0 is a third-party cloud identity and access control management provider. Auth0 Organizations is their solution for SaaS owners to manage customers and partners in their B2B SaaS applications. It allows to manage the business customer’s identity, including single sign-on, role-based access control, user management workflows, and even co-branding login flows. Create Tenants To setup multi-tenant identity and access control management for your SaaS platform, you need to:  Create Auth0 tenants using your Auth0 account. Map all your tenants 1:1 with Auth0 tenants. Auth0 provides Database Connections to authenticate users with their emails/usernames and passwords* (users’ profiles). These credentials can be stored in the Auth0 user store or in your own database. You can have your Auth0 Application read that information after the user logs in. This approach allows users, regardless of which tenant to which they belong to, to log in using in a uniform configuration. Create two Auth0 Applications. The first one is used to allow users to authenticate to the SaaS application. The second one is used to onboard new tenants and invite tenant users. *Different authentication methods can be used within the same tenant: some users authorize through an enterprise identity provider and others by email/password; a separate database connection and stricter security rules may be utilized for the root user within each tenant, while standard tenant users are stored in separate connections.  Onboard New Tenants Using a Registration Form A SaaS registration service (AWS Lambda in this design) orchestrates calls:  Tenant microservice creates a new tenant entry in your backend database. User microservice will be invoked to invite the user to the tenant’s Auth0 Organization. The Auth0 Login Flow Control Access to Resources and Actions Based on the Permissions of the User Assign specific permissions to individual users or create roles with a set of permissions that can be granted to a group of users in an Auth0 organization.  When an application requests an access token for a specific action, it specifies the required scopes. If the user has the appropriate privileges, Auth0 returns an access token with the requested scopes.  Belitsoft SaaS development company specializes in creating multi-tenant applications with custom authentication and authorization modules to protect sensitive information and properly control user access. If you want the best solution for managing tenants and data in your SaaS application, look no further. Contact us today to learn more about how we can help you build a secure and scalable SaaS application. LET'S TALK ABOUT YOUR SAAS PROJECT