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Healthcare Cloud Solutions to Future-Proof Your Business

Cloud-Powered Healthcare Solutions

Cloud Computing

Healthcare interoperability is blocked by absence of connectivity between different implemented IT systems (fragmentation in the healthcare systems, including data silo problem). Healthcare cloud computing is indispensable for creating comprehensive healthcare systems by integrating disparate segments.

Belitsoft’s cloud specialists build custom cloud computing development solutions, such as cloud-based applications, platforms, databases, architectures, servers, and networks. These cloud solutions for healthcare help resolve core healthcare technology challenges.

Skillful Belitsoft’s developers work with SaaS, IaaS, PaaS, DBaaS, and other types of cloud platforms. Developing any cloud solutions, we follow the Cloud Data Management Interface (CDMI) standard.

Cloud computing enables developing the healthcare systems that can be scaled on demand and raise organizational agility.

Thanks to using healthcare cloud computing solutions by Belitsoft, you foster quicker healthcare analytics adoption as well as level up data integration and visualization. In addition, you avoid high upfront investment both in software and hardware.

Thanks to using healthcare cloud computing solutions by Belitsoft, you foster quicker healthcare analytics adoption as well as level up data integration and visualization. In addition, you avoid high upfront investment both in software and hardware.

Cloud Automation and Management

Healthcare cloud automation and management solutions by Belitsoft allow any healthcare organizations to make use of the cloud, regardless of the complexity of their unique processes and requirements. Nonstop monitoring of cloud application functions and its simplified management play the key role in keeping up continuous delivery of healthcare services. Belitsoft provides cloud automation solutions that prioritize user self-provisioning, also known as cloud self-service. It means that every person in a healthcare organization can benefit from using cloud without resorting to IT help on each minor issue.

Belitsoft provides cloud automation solutions that prioritize user self-provisioning, also known as cloud self-service. It means that every person in a healthcare organization can benefit from using cloud without resorting to IT help on each minor issue.

The cloud experts from Belitsoft deliver self-healing cloud solutions. These solutions can instantly detect non-compliant resources, isolate, and restore their compliance.

Our team cuts your expenses by detecting and removing the app bottlenecks. Our team can remove applications vulnerability in different ways. Belitsoft’s specialists apply cloud security management best practices to detect code or architecture level security bottlenecks. Additionally, our specialists recommend the alternative architecture for configuring the application.

Belitsoft cloud maintenance solutions are aimed to provide security assurance. For that, our team tracks and manages critical aspects of software infrastructure security.

Health Systems Engineering

Belitsoft cloud-driven healthcare engineering concentrates on tomorrow's successful solutions to today's challenges of healthcare organizations. Our experienced team builds complex cloud-based custom healthcare software products leveraging deep domain expertise in software engineering. Belitsoft's cloud engineers are proficient in improving data security, boosting reliability and scalability of health software, as well as optimizing its implementation. We cooperate with clients on a remote, onsite, or using mixed models.

Health Systems Engineering

Belitsoft's developers have strong skills in cloud-driven healthcare software engineering, including migration of existing software systems to the cloud platforms like Microsoft Azure, Amazon Web Services, Google Cloud platform, or others.

Our team continuously deploys software in the cloud, adopts cloud security frameworks, and provides leading solutions for strict HIPAA-compliance reporting for healthcare companies.

Our developers offer integration services using cloud APIs of the selected platform to connect interoperable systems.

mHealth Apps Development

mHealth apps integrate electronic health records, and wearable tech devices with mobile applications to inform and educate consumers on healthcare as well as support delivering better care by physicians. Сloud-based computing is the only alternative for such scenarios as enabling resource-heavy real-time remote patient monitoring and big data processing. Belitsoft builds custom cross-platform mHealth applications in the cloud. The apps are compatible both with iOS and Android. Their cutting-edge functionality permits to add new modules and integrate 3rd party software easily.

mHealth Apps Development

The cloud specialists from Belitsoft build Cloud mHealth apps by applying industry-standard cloud computing protocols for device to cloud communication. Сloud-based mHealth apps can execute heavy multimedia and security operations in the cloud, overcoming the current limitations of mobile devices.

Our cloud experts develop cloud-based mHealth apps with an extended set of functions for managing patient medical records, medical billing, appointment booking, population health management, messaging, as well as enhanced interfaces.

Using our mHealth apps development services, you get HIPAA-compliant, integrable apps with an extensive library of modules and complex interfaces.

HIPAA-Compliant Support

Belitsoft is well-versed in providing second-to-none HIPAA-compliant cloud support to healthcare organizations. The specialization of our custom healthcare software development company is data storage, migration, as well as data security, including backup and disaster recovery of data. We help covered entities, such as healthcare providers, insurance companies, healthcare clearinghouses, etc. to get a secure network, meet compliance rules, migrate their data, and manage their workflows through software.
HIPAA-Compliant Support

By strictly following the HIPAA Security rules, we guarantee confidentiality, integrity, and availability, of patients’ electronic protected health information (e-PHI) that covered entities get, create, store, and transfer.

Our cloud experts anticipate and detect potential threats to the integrity or security of critical health data, and protect it.

Together with offering the risk mitigation tailored especially for cloud solutions, Belitsoft guarantees continuous enhancement and updates of security solutions to keep them actionable, feasible, and rational.

Legacy Health Software Migration

Cloud computing has a brand-new solution that allows managing multiple legacy healthcare applications. Thanks to the flexibility and agility of cloud computing, now you can develop, deploy, and scale legacy software applications in the cloud.

Legacy Health Software Migration

Belitsoft offers a fully managed service that includes migrating to the cloud your legacy software with database and all the data. For that, we use top proprietary and 3rd party technologies.

We elaborate a well-though legacy data migration strategy to facilitate the legacy application data migration to the cloud, to remove common legacy data bottlenecks, and keep only the crucial data.

Our cloud experts perform a detailed analysis of different data migration scenarios. Then, we create essential compliance and security scenarios and check if they correspond to the cloud environment.

Belitsoft’s IT team performs incremental data migration and adheres to the lift and shift method, which implies minimal or no changes to the app and data during migration. To assess migration feasibility, we use a cutover time window.

Healthcare Data Analytics

Healthcare data analytics solutions keep on transforming the healthcare industry. Cloud-based healthcare data analytics aimed to revolutionize the process of getting insights from the large volume of complex healthcare data that is drastically growing.

Healthcare Data Analytics

Cloud computing possesses extensive processing and storage potential, which enables deep data analysis of not only text and images, but also video, which is highly demanded, for example, in telehealth solutions.

Cloud-based data analytics solutions are the good way to collect, store, and manage voluminous health data and to perform effective clinical decision support.

Belitsoft’s cloud-powered healthcare data analytics software simplifies the accessibility and sharing potential of healthcare data from any place over the world.

To ensure smooth work of cloud-driven healthcare analytics platforms and improve healthcare interoperability, we remove data duplication/fragmentation and eliminate format incompatibility between different systems.

Our Core Services

Healthcare IT Consulting

Belitsoft´s team provides IT consulting services for insurance and healthcare providers globally. Our healthcare experts specialize in developing enterprise software, pursuing digital innovation through business processes automation.

Developing Enterprise Applications

Belitsoft is highly competent in developing comprehensive enterprise applications. We use an easily scalable, customizable framework tools to address your particular software needs, help you perform digital transformation, and solve your healthcare challenges.

Upgrading Healthcare Solutions

Belitsoft’s developers optimize your healthcare IT system to improve its performance and organization-level outcomes. For that, we upgrade software modules, optimize interoperability with new custom components, and enhance security of your healthcare software.

Providing QA Services

Belitsoft offers software QA services, including usability, security, performance, and other critical types of software testing. This will ensure your healthcare application safety and quality to avoid the clinical workflow disruption.

Healthcare Portfolio

Customization of ready-to-use EHR for individual needs of particular healthcare organizations
Customization of ready-to-use EHR for individual needs of particular healthcare organizations
Belitsoft has helped the Client to customize web and mobile applications that сombine EHR clinical data with patient-generated health data.
Telehealth Software Development for Mental Health Providers
Telehealth Software Development for Mental Health Providers
A founder of a healthcare startup from the USA reached out to us. His idea was to develop a turnkey telemedicine portal that would connect mental/behavioral health professionals and their patients.
Migration from .NET to .NET Core and AngularJS to Angular for HealthTech Company
Migration from .NET to .NET Core and AngularJS to Angular for HealthTech Company
Belitsoft migrated EHR software to .NET Core for the US-based Healthcare Technology Company with 150+ employees.
Custom Healthcare Web Development
Custom Healthcare Web Development
The client's idea was to create a community of people challenged with different diseases to provide live communication among them. The dimensions of the community grow (5000+ members) and that proves the fact that it is a popular site to find friends, share experience and support each other.

Recommended posts

Belitsoft Blog for Entrepreneurs
How Can EHRs Change Life Insurance Industry
How Can EHRs Change Life Insurance Industry
When applying for life insurance coverage, there is a wide range of information that the underwriter will ask. Each question relates directly to the amount of premium you’ll pay. In addition to your name, age, and gender, the company also needs to know about your health status and history. Thus, data on whether you have diabetes or are there cases in your family, as well as any other potential risks, allows insurers to determine the exact amount of a claim. Where do life insurance carriers get client information? The primary source of information for life insurers is the application for coverage. In addition to the medical history, an applicant provides, an underwriter may request reports from their healthcare provider or from other insurance companies to which they have applied for insurance. They may also resort to knowledge bases to obtain additional details on customer background. One such source is the Medical Information Bureau or MIB Inc. If insurers do so, they use the authorization form an applicant signed with their health statement. What is MIB Inc.? MIB Group, Inc. is a non-stock corporation owned by nearly 500 insurance companies throughout the US and Canada. The organization was created in 1902 and is America’s oldest and longest continuously operating credit reporting agency accessing 100 million records and growing weekly. Source: mib.com/facts_about_mib.html MIB Inc. currently owns North America’s largest database of medical conditions on insurance applicants. Collected medicine-related records may include medical conditions, tobacco usage, alcoholism, drug addiction, and personal or family genetic history. The information in MIB’s database is encrypted and may be accessible only to authorized personnel of the member company. They contribute underwriting files to the MIB database that may be helpful to other members. Applicants can also provide the MIB with personal medical records from their healthcare provider that are relevant to the disputing conditions. When customers apply for insurance, they should authorize the use of MIB as an information resource. According to the Federal Trade Commission, MIB is obliged to provide a copy of a consumer’s medical record, if requested, to verify that all information is correct. EHR - a tool to gather data from life insurance applicants The United Services Automobile Association (USAA) is among the first, who let applicants use EHR to simplify the life insurance purchase progress. The Association worked with Cerner to test and implement EHR retrieval technology named HealtheHistory. Source: twitter.com/Cerner/status/847814228694237184 The solution is available to applicants at the Department of Veterans Affairs and Department of Defense. Thus, they are able to deliver their health data directly to the insurer via the patient portal. ‘We can’t emphasize enough how important life insurance is to a financial plan, but we also understand that the process of obtaining a policy continues to introduce challenges industrywide. By using portal retrieval technology and existing EHR platforms, we can provide our members a more secure, easy way to supply records to get a policy decision as soon as possible.’ Dr. Steven Dunlap, medical director at USAA HealtheHistory supports health data collection, encrypts data transmission and limits access to approved members. The tool connects to any accessible patient portal, facilitating the delivery of an applicant’s health history up to 30 days faster than manual retrieval options. Cerner calls this a longitudinal record. The program gathers various sources of raw data, organizes into groups by commonalities, standardizes to match industry terminology, and, as a result, formes a 360-degree view of the applicant. USAA data shows that one of five members do not carry life insurance, with many citing cost and the application process as the top concerns. Recent stats from the marketing research organization LIMRA indicate the number is much higher for the general population, with over 40% of Americans having no life insurance. By deploying EHR technology, USAA is able to speed up the application and underwriting process, without increasing cost for members. MIB Inc.: steps towards EHR integration In April 2018 MIB Inc. released a platform created to automate the acquisition of applicant-authorized EHR for accelerated delivery to the member insurers. The MIB EHR Data Platform have to replace paper-based APS (Attending Physician Statement) retrieval process by offering a unified method through which MIB’s 400 American insurers can securely access health records. The corporation is in current negotiations with leading EHR vendors to facilitate the acquisition and delivery of EHR data for the new EHR Data Platform. This platform will allow the insurance industry to drive process optimization, reduce costs and enhance data-driven decision making. ‘The EHR Data Platform naturally aligns with MIB's competencies in technology, data security, user experience expertise and our large-scale network capabilities. Our mission, and the mandate we have to serve our members, empowers us to deliver an industry-wide data solution that helps all our members drive more rapid issue of life insurance to meet market demands. MIB is owned by the industry we serve—we are the obvious choice for EHR delivery to the life insurance industry.’ Lee B. Oliphant, MIB President and Chief Executive Officer, said Weaknesses of the current system that EHR may resolve Statistics show that 20% of credit histories from the major credit reporting agencies contain errors. However, the percentage of errors in records from the nationwide specialty credit reporting agencies for insurance (e.g. MIB Inc., Ingenix, and Milliman) is unknown. Even though no official data is available, it is estimated that about 5% - 10% of medical report files are inaccurate or contain errors. EHR systems, in turn, are a substantial collection of codified data that appears more credible, since physicians add the info manually. Here insurers can find vital records of allergies, medications, surgical procedures, lab results, as well as social determinants of health. Source: play.google.com/store/apps/details?id=com.epic.haiku.android&hl=en_US EHRs and coded data they contain can drive life insurance underwriting. However, is it a silver bullet for the industry? ‘On top of that, EHRs still don’t do the necessary job of making patient records easily available to providers and patients. EHRs were originally designed as a tool to help with billing, and they are falling short in their ability to provide data in a portable and accessible format. So in many ways, EHRs have merely replaced paper silos with electronic ones, while providers, and the patients they serve, still have difficulty obtaining health records.’ From the speech of CMS Administrator Seema Verma in HIMSS18 Conference, March 3, 2018 Improve EHR system to meet life insurers needs According to Health IT Dashboard 2017, 85.9% of doctors and 96% of hospitals use EHR systems. Patients and physicians have widespread access to the Internet and nearly everyone has a mobile device. All of these benefits provide many access points for viewing and upgrading healthcare data. For the lucky few who get their records, the information is often incomplete, and not always digital or understandable. Customers might be able to get some info in their provider’s portal but if they are consulting different specialists, they might be checking a bunch of portals. As a result, the data is scattered and unstandardized. The existence of multiple technical and terminology standards that serve similar functions is one of the key issues. Thus, the looming shadow of EHRs interoperability will be settled more quickly. Plus, communities join into a single data sharing network, in which each participant makes one connection to the web and then can access records from all parties. This is the way Carequality have created a standardized, national-level interoperability framework to link all data sharing networks. Human API released a new version of their medical data platform for life insurance carriers. The solution leverages the company's nationwide network of EHR, pharmacy, and lab integrations to deliver electronic health data for more efficient underwriting. ‘We started with two simple questions: "why can't consumers access their health data?" and "why do enterprises struggle to connect this data? These questions inspired our mission to create data liquidity throughout the healthcare ecosystem.’ from Human API website The platform includes the Enterprise Portal, where insurers can request real-time access to medical records, view the results in a longitudinal timeline, and leverage a robust clinical data flow to automate underwriting decisions. With no IT integration required, the solution enables insurance carriers to incorporate Human API into their current underwriting programs. The adoption of electronic health records has increased rapidly in recent years, opening the door for new approaches to medical record retrieval and direct consumer engagement. ‘Attending physician statements (APS) have always been the gold standard for underwriting, but they take too long and cost too much. The ability to directly access EHR data will be the biggest game changer for underwriting in my now very long career. Companies who figure out how to access digital EHR data first will find a distinct competitive edge in the marketplace’ Jennifer Richards, the Head of Life Insurance New Business and Underwriting at Mass Mutual Conclusion Despite the comparatively large hundreds-years-old databases, EHR information has the potential to essentially shatter and improve the life insurance risk assessment process. More and more companies made the investment to incorporate EHRs into their automated underwriting programs. The benefits are clear - EHR can reduce application time while improving customer experience. Hire Belitsoft - a top offshore software development company!
Alex Shestel • 6 min read
How to Design a Healthcare App
How to Design a Healthcare App
Mobile health is a growing IT sector that focuses on transforming how healthcare providers interact with their patients. According to Zion Market Research, the global mHealth market size is expected to reach $102.43 billion by 2022. Industry experts predict that 70% of healthcare organizations will invest in healthcare mobile app development by 2018. ‘The market for digital health tools is finally starting to catch up to the demand. Unfortunately, this increase can lead to a surge in unreliable tools. Nearly half of consumers today are considered digital health adopters—and that number is only going to rise as the benefits become apparent and tech-savvy generations get older.’ Nitin Goyal, MD, Orthopaedic Surgeon, Founder & CEO at Pulse Platform The following statistic displays the number of mHealth apps available on Google Play. During the last measured period, the store offered just over 51.000 medical apps, representing a 5.7 percent growth over the previous quarter. The most income-generating mHealth apps on Google Play are fitness and calories counter systems. Source: statista.com/statistics/779919/health-apps-available-google-play-worldwide Apps have become an essential part of the healthcare field. Medicare providers and patients all benefit from up-to-date, user-friendly, and free or minimal-cost healthcare apps. Due to rising supply, it has become increasingly important to offer a high-quality product. Thus, designing mHealth apps that provide efficient and convenient ways of providing healthcare services is among the top-most concerns of developers. In this article, we talk about what colors are best suited for healthcare mobile software and give you some examples. We then focus on app notification design. Surely we compare iOS and Android design style and show you some key differences with actual screenshots. Finally, we refer to best practices to customize your app for users with disabilities. Have an idea to create an mHealth app? Contact us to start your business! Familiarizing with colors As part of the development process, it is necessary to choose a proper color scheme and fonts. Users should feel peaceful with confidence that they run a right medicare software to address their needs and concerns. To that end, vibrant colors should be replaced with a more delicate and calm color palette. Fortunately, there are many hues to pick from both cold and warm sides of the spectrum. Cold tones are most often used for the background. These hues establish an overall sense of tranquility that is necessary to help users concentrate on the more important features of the healthcare app. White Fitbit - an activity tracker for iOS and Android Oscar Health - a health insurance app for iOS and Android Blue Calm - a meditation app A pharmacy app by Lewis+Humphreys Grey BioDigital - a 3D health visualization system Clue - a female health app Green Omada - a behavior change program HealthTap - an online doctor consultation app Warm tones are great for accent colors and for attracting attention. However, products designed in this color scheme as their dominant may be used in obstetrics and gynecology. All because in Europe and the USA, pink is often associated with the women. Pink Flo - a fertility and pregnancy calendar Blogilate - a fitness app Purple Zipdrug - a medication delivery app Cliniklik healthcare app design by Pablo Barzet, Source Yellow GoodRX - a drug price tracking app in the USA Red Pills On Time - a medication reminder and pills tracker Orange MINDBODY - fitness, salon and spa booking app App notification design Notifications are crucial to mHealth apps, especially for those that provide tracking and reminders. Giving them different designs enables indicating importance and urgency. For example, an app reminds users of their scheduled time to take a pill. Along with this, it also notifies of an upcoming physician’s appointment. To avoid confusion and highlight relevance, developers have to give the reminders various design elements. To this end, they can use color-coding, font choices, gestures, or notification behavior/animation. CareZone - a medication management app MyTherapy - a medication reminder and pill tracker Couch to 5K - a running trainer Sleep Cycle - an intelligent alarm clock For our take on healthcare software development take a look at the latest EHR we've developed. This article give insights into the approach we used: How to Build an EHR System Android vs iOS: Different design styles Most popular apps, including mHealth, are released for both iOS and Android. The following are some differences to be taken into account when developing a mobile app. — First off, the design rules for Android devices are determined by Material Design, while for iOS - by Human Interface Guidelines. The first one is based on a layered "paper" approach providing more hierarchy with realistic shadows, light, and motion. As for iOS, designers can use the effects of transparency, blurring, gradients or shadows to attract users attention. — Moving between screens is a common action users take on apps. On Android, there is a universal navigation bar at the bottom. The back button is the simplest way to go back to a previous screen and it works in all apps. Runtastic Balance Food Tracker and Calorie Counter for Android The vision on iOS is a little different. As can be seen on the screenshot below, there is no back button here. Thus, the app screen has a button on the top left corner. Moreover, designers can also use the name of the previous page behind the back icon to let customers know where they will go back. Runtastic Balance Food Tracker and Calorie Counter for iOS In addition, Apple introduced a gesture of swiping from left to right in apps to go back. The animation for the collecting samples flow for Bloodline for iOS by Bryce Thompson, Source — Apps have different areas within them, usually organized as tabs. Different sections on Android are displayed on top of the app. In addition, the Android version shows only icons on the tab row, whereas the iOS version also has labels. However, iOS app’s sections are organized as tabs on the bottom of the screen. Doctor On Demand for Android Doctor On Demand for iOS MyFitnessPal for Android MyFitnessPal for iOS — Action buttons are those that enable users to take some actions like share, upload/download etc. Both Android and iOS have their own icon styles. My Diet Coach - a weight loss motivating and tracking app for Android My Diet Coach for iOS Understand target audiences A proper quality design is important for any mobile app, but it’s especially vital when creating a program for sensitive target users. ‘I’m not the first entrepreneur to create a digital health app for patients. But as a surgeon, I’m very aware of the day-to-day issues that arise, including the nuanced relationship between patient and provider. Not all entrepreneurs in digital healthcare have this level of awareness. That means some digital health tools don’t consider a patient’s best interest.’ Nitin Goyal said The following are some obstacles disabled users have met: Blind people may use screen reader software or Braille devices to access content but only text-based. Deaf users cannot access audio content unless it is transcribed. People who can’t use a mouse have to able to access content with a keyboard alone. Users with low vision, dyslexia, or attention deficit are difficult to process extensive texts and require more white space, simple screen images, and proper color contrast. Examples of color disabilities Site: w3.org/WAI/GL/low-vision-a11y-tf/wiki/Overview_of_Low_Vision Since this field is mainly represented by older people, or who might have sensory impairments and other disabilities or technically challenged, it’s necessary to tailor healthcare mobile app design. Source: greatcall.com/greatcall/lp/is-mobile-healthcare-the-future-infographic.aspx Designing for low- and no-vision and hearing: legislation Developing software that can be used by all people without the need for adaptation or specialized design is called “universal design”. Many software companies, unfortunately, focus on the characteristics of the “average” user. ‘The term user experience is now widely used, especially by major players in the industry including Apple, IBM and Microsoft. However, in many cases, the term is contrasted to usability which is often depicted as a much narrower concept focusing on systems being easy to use.’ Tom Stewart, Chair of the ISO sub-committee With a view to making software accessible for people with disabilities, the U.S. Congress has passed legislation in a range of areas. Section 504 of the Rehabilitation Act of 1973 and its amendment 508 suggested in 1986 require that information technology funded/used by the federal government must be designed to be accessible to people with disabilities. The Americans with Disabilities Act of 1990 (ADA) and ADA Amendments Act of 2008 require public software be accessible to users with physical, sensory, or cognitive disabilities, regardless of what audience is targeted. In 2017, the U.S. Access Board published a final rule updating accessibility requirements for information and communication technology (ICT). Further, it boosts international harmonization, in particular with Canada, Germany, France, Australia, New Zealand, and Japan. Designing for low- and no-vision and hearing: best practices The “mobile accessibility” standards address devices that interact with the web, including smartphones, tablets, and wearables. Most often, mobile devices have a small screen size that limits how much information users can actually view at one time. Especially, when zoom is used by people with low vision. Some best practices for helping low-vision users to make the most of small screens include: Cut the amount of the displayed content by providing a dedicated mobile version (providing fewer content modules, fewer images, or focus on important mobile usage scenarios) or a responsive design (on narrow screens the navigation menus may be hidden until a user taps a menu button). The left picture shows a page with no modification, print preview at 100%. The picture on the right shows the same page at 200%. Source: w3.org/WAI/GL/low-vision-a11y-tf/wiki/Printing_Customized_Text Provide a reasonable default size for content and touch controls to prevent text magnification by the user. The content has to be resizable without assistive technology up to 200 percent. Supply with on-page controls to change the text size (e.g. magnifying lens view under user’s finger). Source: pcworld.com/article/3131925 Avoid using complicated and decorative fonts because they can be discerned much harder. Use standard fonts like Arial or Times New Roman instead. Create alternative CSS with a highly contrasting color scheme. The WCAG 2.0. suggests Minimum (at least 4.5:1 or 3:1 for large-scale text) and Enhanced (at least 7:1 or 4.5:1 for large-scale text) contrasts. Arrange interactive elements where they can be easily seen when the device is held in different positions. Use a range of clearly contrasting colors and hues instead of relying on black and white as the design's only contrasting colors. Type1 Diabetes Mobile App Design Source: behance.net/gallery/32173645/Type-1-Diabetes-Mobile-App Avoid hard-to-see color combos, e.g: Green & red. Green & brown. Blue & purple. Green & Blue. Blue & Grey.  Green & Grey. Indicate clearly interactive elements (buttons or links) from non-actionable elements (content, status info, etc.) through the following parameters: Conventional shape: rounded-corner shaded button shape. Iconography:  question mark, home icon, back arrow, etc. for conventional visual icons. Color offset: various text color, shape with a different background color to distinguish the element from the page background. Conventional style: underlined text and different colors for links. To ensure access to all potential audiences, it is essential that software companies develop products to be compatible with assistive technology. When a mobile app is designed to be accessible to users with a broad range of disabilities, the others benefit too. For example, video transcripts enable deaf users to access the content. However, this feature allows for viewing it in a noisy environment, or for whom English is a second language. User-friendly design An important step toward a successful UX design for mHealth apps is to practice simplicity. We’ve reviewed dozens of healthcare apps, both on Google Play and App Store, and noticed there is one thing in common: simplistic or minimalist design. Indeed, such programs give users a clean and professional experience. ‘Don’t re-invent the wheel. Use models and frameworks developed by others, and modify them as needed [...] and incorporate what has worked before. Once that framework is established, it’s easier to go in and add elements that personalize or brand the experience.’ Jeffery Kendall, SVP and GM at Kony Health tracking app by Jakub Antalík Source: dribbble.com/shots/2834322-Health-tracking-app-case-study It is also essential to facilitate the register/sign-in process. User authorization has to take the minimum amount of time avoiding numerous screens and clicks. The simplification principle also applies to emergency data accessing, like doctor phone number, prescription medication, or allergies. Icon sets used in healthcare apps should be highly intuitive so that users can easily understand what a particular icon means. However, more creative symbols can be added while including their description at hand. Thus, developers avoid the possibility of confusing their customers. Medical Icon Set by Vivek Karthikeyan Source: dribbble.com/shots/3121056-Medical-Icon-Set Medical Icons by Asif Hussain Source: dribbble.com/shots/4131178-Medical-Icons Complete Medical App by Asif Hussain Source: dribbble.com/shots/4078004-A-Complete-Medical-Application Conclusion Designing an effective healthcare mobile app requires focusing on what works best for target audiences. Design should be centered around an intuitive UI/UX and proper coloring schemes. Moreover, to reach a wide audience, mHealth apps should be tailored for sensory impairment users. You want your app to have a modern and harmonious look? Our talented designers will liven it up! Contact us for advice!
Alex Shestel • 8 min read
EHR Data Analytics Solutions
EHR Data Analytics Solutions
Before Extration To host and manage healthcare data for analytical purposes, a separate healthcare analytics database is needed. The raw EHR database data should be converted, preferably adopting the OMOP Common Data Model, to enable systematic analysis with standard analytic tools. Raw EHR databases are usually optimized for fast data entry and retrieval of individual patient records, not for complex analysis. Creating a separate database specifically for analysis can improve query speed and reduce the load on your operational EHR system. Database system development includes database design, implementation, and database maintenance.  Healthcare analytics database design  Conceptual Data Model This is an abstract representation of the data and connections between distinct entities (such as patients, visits, medications) without being tied to a particular database system. Specification of a logical schema The logical schema defines each table needed in your database, like "Patient", "Medication", "Diagnosis". It includes Columns (or fields/attributes) that determine what information goes into each table, such as patient name and date of birth). The Datatypes of the columns, like text, numbers, or dates, are also specified, along with any Constraints like Primary Key - a unique identifier for each row in a table, such as patient ID. Healthcare analytics database implementation This involves creating the actual database based on the logical schema. Examples include optimizing data storage for better performance, implementing security measures to safeguard data, and establishing user interactions with specific data segments. Healthcare analytics database maintenance This entails ensuring the database continues to perform well and adapt to changing needs. Monitoring performance and addressing issues, making changes to the structure as needed, effective communication between healthcare database administrators, developers, and users to determine necessary changes. Our healthcare software development services handle complex challenges of healthcare data analytics, ranging from data extraction to the application of advanced statistical and machine learning techniques. Contact our experts for deeper data insights. Difference between EMR and EHR data Electronic medical records (EMRs) digitize the traditional paper charts found within a specific hospital, clinic, or doctor's office.  Electronic health records (EHRs) are much more comprehensive, as they include all the data found in EMRs as well as information from labs, specialists, nursing homes, and other providers. EHR systems share this data across authorized clinicians, caregivers, and even patients themselves, allowing for coordinated, patient-centered care regardless of location. Besides patient care, EHR data serves administrative and billing purposes.  Recently, EHRs have become a major source of real-world evidence, aiding in treatment evaluation, diagnosis improvement, drug safety, disease prediction, and personalized medicine. We collaborated with a US healthcare solutions provider to integrate EHR with advanced data analytics capabilities. Our integration streamlined data management, empowered healthcare providers, and optimized care delivery processes, resulting in improved patient outcomes and operational efficiency. Check out our case to learn more. The complexity of EHR data demands a multidisciplinary team to handle the challenges at every stage, from data extraction and cleaning to analysis. This team should comprise experts in database, computer science/informatics, statistics, data science, clinicians, epidemiologists, and those familiar with EHR systems and data entry procedures. The large volume of EHR data also causes significant investment in high-performance computing and storage. For more information on effectively leveraging EHR data and healthcare analytics, explore our comprehensive guide on EHR Implementation. Improve patient care and streamline operations with our EHR/EMR software development. From seamless data integration to intuitive user interfaces, our team of dedicated healthcare app developers can tailor to your needs. Get in touch for project planning and preliminary research. Traditional Relational Database Systems  EHR data often fits well into the table format (patients, diagnoses, medications, etc.). Relational models easily define how different entities link together (a patient has multiple visits, each visit has lab results, etc.). Constraints offered by relational databases help maintain data accuracy.  Oracle, Microsoft SQL Server, MySQL, and PostgreSQL are widely used relational databases in healthcare. Distributed Database Systems   As databases grow massively, traditional systems struggle with performance, especially for analysis and complex queries. Apache Hadoop: The Framework Hadoop lets you spread both storage and computation across a cluster of commodity (regular) computers. The Hadoop Distributed File System can reliably store massive amounts of data on multiple machines. It also offers a programming model for breaking down large-scale analysis tasks into smaller parallel chunks. Apache HBase: The Real-Time, Scalable Database Apache HBase, on the other hand, uses HDFS for storage and is a non-relational database. It is designed to handle semi-structured or unstructured data, borrowing principles from Google's Bigtable solution for managing massive datasets. It enables fast retrieval and updates on huge datasets. NoSQL (like HBase, MongoDB, Cassandra DB) vs. Traditional SQL Databases NoSQL databases excel at handling images, videos, and text documents that don't fit neatly into predefined tables. They store data as "documents" (similar to JSON), providing flexibility in the structure of information stored in a single record. However, NoSQL databases prioritize horizontal scalability (adding more machines to store more data) and may sacrifice some consistency guarantees compared to traditional SQL databases. Data Extraction in Healthcare Inclusion/exclusion criteria may consider patient demographics like age, gender, or race. It can also involve extracting data from various tables in EHR/EMR systems, such as medication, procedure, lab test, clinical event, vital sign, or microbiology tables. However, some of these data or variables may have high uncertainty, missing values, or errors. To aid, Natural Language Processing (NLP) techniques can be employed. NLP can analyze text data within EHR/EMR systems to identify relevant mentions that may not be directly linked to expected keywords or codes but are important for analytics purposes. Moreover, accurately identifying missing relationships based on indirect evidence requires substantial domain knowledge. Cohort Identification  Cohort identification selects patients to analyze based on diagnoses, procedures, or symptoms.  Careful definition of the cohort is essential to avoid mixing patients who are too different. Without a well-defined cohort, the analysis will not yield useful insights about any group. Identifying your research cohort in EHR data can be tricky due to input errors, biased billing codes, and missing data.   Phenotyping methods and data types Rule-Based Methods for Cohort Identification ICD codes are a starting point for identifying patients. When studying conditions like heart attacks (acute myocardial infarction), it may seem logical to search for ICD codes specifically linked to that diagnosis. However, relying solely on ICD codes, especially for complex diseases, is often not sufficient. It is important to note that ICD codes are primarily used for billing. Doctors may choose codes that are more likely to get reimbursed, rather than the code that precisely reflects a patient's complex condition. The condition's severity, complications, and management are important factors not easily represented by one code. Errors in data entry or delayed diagnoses can lead to patients having incorrect codes or missing codes. Machine Learning Methods for Cohort Identification Machine learning algorithms can be trained to spot patterns in complex EHR data that may go unnoticed by humans, potentially finding patients that traditional rules might overlook. Clinical notes contain detailed patient information that is not easily organized into codes. NLP techniques help computers understand human language within these notes. Key Tools and Methods MedEx. A specialized NLP system designed to extract medication names, dosages, frequencies, and other relevant information. CLAMP. A broader toolkit that supports various NLP tasks in the medical domain, like identifying diagnoses or medical procedures within the text. OHNLP. A resource hub providing researchers with access to a variety of NLP tools, thereby facilitating their implementation. Complex models like Recurrent Neural Networks (RNNs) can effectively identify patterns in large datasets with many variables and patient records. Bayesian methods can help determine disease groups, even in situations where perfect data for comparison is unavailable. The FSSMC method helps cut down the number of variables you need to consider and ranks them based on their predictive utility for disease identification. Methods like clustering can group patients based on similarity, even without predefined disease labels. Simpler approaches can also be used in healthcare analytics for data extraction and transformation. One method is to define data requirements and use ETL pipelines. These pipelines extract data from different sources, transform it, and load it into a target database or data warehouse. ETL pipelines are efficient for processing large volumes of data, ensuring data integrity and consistency for analysis and reporting. While not as advanced as NLP or machine learning, these methods still provide valuable insights and practical solutions for organizations to leverage their data effectively. Leverage your healthcare organization's data analytics with our tailored healthcare business intelligence solutions. Our expert team employs advanced strategies to derive actionable insights from your clinical records and diverse data sources. Contact us now for advanced analytics to improve operations. Data Cleaning in Healthcare The primary purpose of EHR databases lies in supporting the daily operations of healthcare, such as billing, legal documentation, and user-friendliness for clinical staff. However, this singular focus presents challenges for analytics.   The purpose of data cleaning is to ensure that the analysis conducted is meaningful and focused on answering analytics questions, rather than battling errors or inconsistencies. This process aims to achieve a more uniform distribution of lab values. Various tasks fall under data cleaning, such as eliminating redundancies, rectifying errors, harmonizing inconsistencies in coding systems, and standardizing measurement units. Consolidating patient data from various clinical visits that have conflicting records of race, gender, or birthdate. Harmonizing disease diagnosis, procedures, surgical interventions, and other data that may be recorded using varied coding systems like ICD-9, ICD-10, or ICD-10-CM. Correcting variations in the spelling of the same medication's generic names. Standardizing the units used for lab test results or clinical measurements that vary across different patient visits. Data cleaning is essential for the entire EHR database to support all types of projects and analyses, except for projects that focus on studying errors in data entry or management.  Data cleaning methods should be tailored to the specific errors and structure of each EHR database. The provided methods serve as a foundation, but must be customized for each project. The first data cleaning project is usually the most time-consuming, but team experience with the database and common errors can help speed up the process for later projects. EHR data cleaning tools Many existing tools address datasets from specific healthcare facilities or focus solely on one aspect of data cleaning (like standardizing units). Some tools might be better suited for project-specific fine-tuning rather than broad database cleaning. Data Wranglers Data wranglers are tools specifically designed to handle diverse data types and offer transformations like reformatting dates, handling missing values, and pattern detection. Examples: DataWrangler (Stanford) and Potter's Wheel (UC Berkeley). They work with many data formats, help users understand big datasets quickly, and have optimized code for handling large datasets. While adaptable, they might not address the specific complexities and inconsistencies found in EHR data. Specialized EHR data cleaning tools may be necessary for the best results.  Data Cleaning Tools for Specific EHR Datasets  EHR databases can differ in сoding systems (e.g., ICD-10 vs. ICD-10-CM), date formats (European vs. US style), address Formats (country-specific). Because of this, data cleaning tools often need to be tailored to specific EHR database systems. It is unlikely that a single tool will universally apply to all databases. Even if certain tools aren't directly transferable, researchers can still learn valuable cleaning methods and approaches by studying tools like the "rEHR" package. rEHR package acts as a wrapper for SQL queries, making it easier for researchers to work with the EHR database. Statistical data cleaning methods also exist. For example, the Height Cleaning Algorithm detects and removes unlikely height measurements (like negative changes) based on median values across life stages. This algorithm is relatively simple to implement and catches many errors. But there are risks removing rare, but valid, data points (e.g., post-surgery height changes). Healthcare Data Quality Assessment Here's a summary of data quality metrics for assessing EHR data. Checking if data values are within expected ranges and follow known distributions. For example, pulse oximetry values should be between 0 and 100%. Verifying the soundness of the database structure, such as securing each patient, has a unique primary key. Ensuring consistent formatting of time-varying data and logical changes over time. Examining for logical data transitions. For instance, there should be no blood pressure measurements for a patient after their recorded death. However, it is important to note that rare exceptions may exist. Evaluating relationships between attributes, such as confirming a male patient does not have a pregnancy diagnosis. Common EHR Data Errors and Fixing Methods Cleaning methods primarily target tables containing numerical results from encounters, labs, and clinical events (vital signs). Issues with diagnosis codes, medication names, and procedure codes also can be addressed. Demographics Table The demographics table is the cornerstone of data quality assessment. Fixing Multiple Race and Gender Data analysis relies on unique identifier codes for individuals, especially sensitive personal information like medical records, instead of using actual names or identifying information. This is done to protect patient privacy and anonymize the data. It functions as a random ID tied to individuals or samples in the dataset, maintaining their anonymity. "Patient Surrogate Key" (Patient SK) is the unique key for each patient in a medical dataset. Data analysts can track patient records, test results, treatments, etc. without exposing personal information. Multiple demographic entries in a patient's records may have conflicting race or gender information. This is how we fix race/gender inconsistencies: Gather all Patient IDs linked to a given Patient SK, collecting all demographic data associated with that individual. Discard entries with missing race or gender (NULL, etc.) as they are likely incomplete or unreliable. If a clear majority of the remaining entries agree on a race or gender, assign that as the most probable value for the patient. If there is no clear majority, default to the earliest recorded value as a likely starting point. Fixing Multiple Patient Keys for the Same Encounter ID   The error of linking multiple unique patient identifiers (Patient SKs) to the same Encounter ID undermines the EHR database's integrity. If this error is widespread, it reveals a fundamental problem with the database structure itself, requiring a thorough investigation and potential restructuring. If this error occurred rarely, the affected records may be removed. Fixing Multiple Calculated Birth Date   In the healthcare database under analysis, patient age information may be stored across multiple fields—years, months, weeks, days, and hours. There are three scenarios for recording a patient's age: All age fields are blank, indicating missing age information. Only the "age in years" field is filled, providing an approximate age. All age fields (years, months, weeks, days, hours) are filled, allowing for precise calculation of the patient's age. It is important to consider that each patient's records may cover multiple visits, and the age values may vary between these visits. To determine the accurate birth date, we follow a systematic procedure: If all recorded ages are blank, the birth date is missing and cannot be calculated. If all ages have only the years filled, we either use the birth year indicated by the majority of encounters or the first recorded age in years as an approximation of the birth year. If at least one encounter has all age fields filled (third scenario), we calculate the birth date from the first such encounter.   This procedure ensures that we derive the most accurate birth date value possible from the available data fields. Lab Table Large EHR databases are used by multiple healthcare facilities. Each facility may use different kits or equipment to evaluate the same lab measurement. This leads to varying normal reference ranges for measurements, like serum potassium level. Additionally, EHR system providers allow each facility to use customized data entry structures.  These two factors resulted in multiple formats being used to report the same lab measurement.  For example, in one dataset, serum potassium level was reported using 18 different formats! Another major issue plaguing EHR data is inconsistency during data entry.  In an example database, it was noticed that some electrolyte concentration levels were incorrectly reported as "Millimeter per liter" instead of the common "Millimoles per liter" format.  Another common mistake is mixing and confusing the lab IDs for count versus percentage lab results.  This is prevalent in measurements related to White Blood Cells (WBC). For example, the database can have different lab ID codes for Lymphocyte Percentage (measured as a percentage of the total WBC count) and the absolute Lymphocyte Count. However, due to operator misunderstanding or lack of awareness, the percentage of lymphocytes is sometimes erroneously reported under the lab ID for the lymphocyte count, with the unit of measurement also incorrectly listed as a percentage. Instead of deleting these mislabeled values, which would increase the amount of missing data and introduce bias, we can develop a mapping table approach. This involves creating a conversion map to consolidate the data and make the reporting format uniform across all entries. Specifically, we can map the mislabeled percentage values to their appropriate lab ID code for the lymphocyte percentage. By employing this mapping, we are able to resolve the data entry errors without losing valuable data points. Developing Conversion Map Flow chart of the lab unit unification algorithm Conversion map example The conversion map is a table that helps us convert lab data from different formats into a unified representation. We use mathematical formulas in the Conversion Equation column to transform the original values into the desired format. If the original and target formats have similar distributions, no conversion is necessary. But if they are different, we need to find the appropriate conversion equation from medical literature or consult with clinicians. To handle extreme or invalid values, we incorporate Lower and Upper Limits based on reported value ranges in medical journals. Values outside these limits are considered missing data.   General strategies for managing the output of the data cleaning process When working with large EHR datasets, it is necessary to keep the unique identifiers in your output unchanged. These identifiers are required for merging data tables during subsequent analyses. It is also advised to be cautious when deciding to remove values from the dataset. Unless you are certain that a value is an error, it is recommended not to drop it.   To maintain a comprehensive record of the data cleaning process and facilitate backtracking, we save the results and outputs at each step in different files. This practice helps you keep track of different file versions. When sharing cleaned data with different teams or data analysis users, it is helpful to flag any remaining issues in the data that could not be addressed during cleaning. Use flags like "Kept," "Missing," "Omitted," "Out of range," "Missing equation," and "Canceled" for lab data. Clinical Events The clinical event table, specifically the vital signs subgroup, has a similar structure to the lab table in EHR databases. So, you can apply the same steps and approaches from the data cleaning tool to the clinical event table. However, it is important to note that this table may also contain other inconsistencies. Variable Combining   In the clinical event table, a common issue is the use of unique descriptions for the same clinical event. This happens because multiple healthcare facilities use the database, each with their own labeling terminology. To tackle this challenge, statistical techniques and clinical expertise are used to identify events that can be combined into one variable. For instance, there are many distinct event code IDs for the Blood Gas test, some with similar descriptions like "Base Excess," "Base Excess Arterial," and "Base Excess Venous." Once expert clinicians confirm these labels can be combined, a decision can be made to consolidate them into a single variable.   Medication Table Medication tables present their own unique challenges and inconsistencies that require different strategies. The data in the Medication table consists mainly of codes and labels, not numerical values. When working with this table, using generic medication names is more efficient than relying solely on medication codes (like National Drug codes). However, even within the generic names, there can be inconsistencies in spelling variations, capitalization, and the use of multiple words separated by hyphens, slashes, or other characters.  Procedure Table Procedure codes identify surgical, medical, or diagnostic interventions performed on patients. These codes are designed to be compatible with diagnosis codes (such as ICD-9 or ICD-10) to ensure proper reimbursement from insurance companies, like Blue Cross Blue Shield or Medicare, which may deny payment if the procedure codes do not align with the documented diagnosis. Three types of procedure codes are commonly used.  ICD-9 procedure codes Consist of two numeric digits followed by a decimal point, and one or two additional digits. They differ from ICD-9 diagnosis codes, which start with three alphanumeric characters. ICD-9 procedure codes are categorized according to the anatomical region or body system involved. CPT (Current Procedural Terminology) codes Also known as Level 1 HCPCS (Healthcare Common Procedure Coding System) coding system, CPT codes are a set of medical codes used to report medical, surgical, and diagnostic procedures and services. Physicians, health insurance companies, and accreditation organizations use them. CPT codes are used in conjunction with ICD-9-CM or ICD-10-CM numerical diagnostic coding during electronic medical billing. These codes are composed of five numeric digits. HCPCS Level II codes Level II of the HCPCS is a standardized coding system used primarily to identify products, supplies, and services, such as ambulance services and durable medical equipment when used outside a physician's office. Level II codes consist of a single alphabetical letter followed by four numeric digits. The data cleaning for the procedure table often may not be necessary. The data analysis framework, which involves multiple steps iteratively Healthcare Data Pre-Processing   Variable Encoding   When working with EHR datasets, the data may contain records of medications, diagnoses, and procedures for individual patients.  These variables can be encoded in two ways:  1) Binary encoding, where a patient is assigned a value of 1 if they have a record for a specific medication, diagnosis, or procedure, and 0 otherwise.  2) Continuous encoding, where the frequency of occurrence of these events is counted.   Tidy Data Principles  Variable encoding is a fundamental data pre-processing method that transforms raw data into a "tidy" format, which is easier to analyze statistically. Tidy data follows three key principles: each variable has its own column, each observation is in one row, and each cell holds a single value.  Variables are often stored at different tables within the database. To create a tidy dataset suitable for analysis, these variables need to be merged from their respective tables into one unified dataset based on their defined relationships. The encounter table within an EHR database typically already meets the tidy data criteria. However, many other tables, such as the medication table, often have a "long" data format where each observation spans multiple rows. In these cases, the long data needs to be transformed. A diagram illustrates how the principles of tidy data are applied. Initially, the medication table is in a long format, with multiple treatment variables spread across rows for each encounter ID To create a tidy dataset, we follow a few steps: Each variable is put into one column. The multiple treatment variables in the medication table are transformed into separate columns (Treatment 1, Treatment 2, Treatment 3, Treatment 4) in the tidy data. This ensures that each variable has its own dedicated column. Each observation is in one row. The encounter table already has one row per encounter observation. After merging with the transformed medication data, the tidy dataset maintains this structure, with one row representing all variables for a single patient encounter. Each cell has a single value. In the tidy data, each cell contains either a 1 (treatment given) or 0 (treatment not given). This adheres to the principle of having a single atomic value per cell. The merging process combines the encounter table (with patient ID, encounter ID, age, sex, and race variables) and reshaped medication data to create a final tidy dataset. The merging process combines the encounter table and reshaped medication data to create a final tidy dataset. Each row corresponds to one encounter and includes relevant variables like treatments, demographics, and encounter details. Feature Extraction: Derived Variables  Сertain variables, such as lab test results, clinical events, and vital signs, are measured repeatedly at irregular time intervals for a patient Instead of using the raw repeated measurements, feature extraction and engineering techniques are applied to summarize them into derived feature variables.  One common approach is to calculate simple summary statistics like mean, median, minimum, maximum, range, quantiles, standard deviation, or variance for each variable and each patient. Let's say a patient's blood glucose levels are recorded as follows: 90, 125, and 100. Features such as mean glucose (105), maximum glucose (125), and glucose range (35) could be implemented. Derived feature variables can also come from combining multiple original variables, such as calculating body mass index from height and weight.  Additionally, features related to the timing of measurements can be extracted, such as the first measurement, the last measurement, or measurement after a particular treatment event. The goal is to extract as many relevant features as possible to minimize information loss. Dimension Reduction  Variable Grouping or Clustering Many EHR variables, such as disease diagnoses, medications, lab tests, clinical events, vital signs, and procedures, have high dimensions. To reduce data complexity, we can group or cluster these variables into higher-level categories. This also helps to ensure a sufficient sample size for further analysis by combining smaller categories into larger ones. For example, the ICD-9-CM system comprises over ten thousand diagnosis codes. However, we can use the higher-level ICD-9-CM codes with only three digits, representing less than 1000 disease groups.  Healthcare Data Analysis and Prediction Statistical Models  EHR datasets are big, messy, sparse, ultrahigh dimensional, and have high rates of missing data. These characteristics pose significant challenges for statistical analysis and prediction modeling. Due to the ultrahigh dimensionality and potentially large sample sizes of EHR data, complicated and computationally intensive statistical approaches are often impractical. However, if the dataset is properly cleaned and processed, certain models, like general linear models, survival models, and linear mixed-effects models, can still be appropriate and workable to implement. Generalized linear models (GLMs) are commonly used and effective for analyzing EHR data due to their efficiency and availability of software tools. For time-to-event analysis, survival regression models are better suited than GLMs, but they need to account for issues like missing data and censoring in EHR data. Mixed-effects models are useful for handling longitudinal EHR data with repeated measures and irregular timing. Dealing with the high dimensionality is a major challenge, requiring techniques like variable screening (SIS), penalized regression (LASSO, Ridge), and confounder adjustment methods. Large sample sizes in EHR data pose computational challenges, requiring approaches like divide-and-conquer, sub-sampling, and distributed computing. Neural Network and Deep Learning Methods Deep learning (DL) is a class of machine learning techniques that uses artificial neural networks with multiple hierarchical layers to learn complex relationships between inputs and outputs. The number of layers can range from a few to many, forming a deeply connected neural network, hence the term "deep" learning. DL models have input, hidden, and output layers connected through weights and activation functions. DL techniques are increasingly applied to various aspects of EHR data analysis due to their ability to handle high dimensionality and extract complex patterns. Deep learning approaches can be categorized as supervised learning for recognizing numbers/texts from images, predicting patient diagnoses, and treatment outcomes, and unsupervised learning for finding patterns without predefined labels or target outcomes. Supervised learning is the most developed category for EHR data analysis. DL has some advantages over classical machine learning for EHR data: Can handle both structured (codes, tests) and unstructured (notes, images) data Can automatically learn complex features from raw data without manual feature engineering Can handle sparse, irregularly timed data better Can model long-term temporal dependencies in medical events Can be more robust to missing/noisy data through techniques like dropout However, DL models require careful hyperparameter tuning to avoid overfitting. Types of Deep Learning Networks Multilayer Perceptron (MLP) The foundational DL model, with multiple layers of neurons. Good for basic prediction tasks in EHR data. Convolutional Neural Network (CNN) Excels at analyzing data with spatial or local relationships (like images or text). Used for disease risk prediction, diagnosis, and understanding medical notes. Recurrent Neural Network (RNN) Designed for sequential data (like EHRs over time). Can account for long-term dependencies between health events. Used for disease onset prediction and readmission modeling. Generative Adversarial Network (GAN) A unique approach where two networks compete. Used for generating realistic synthetic EHR data and disease prediction. Choosing the Right Architecture CNNs are great for images and text. GANs offer more flexibility (data generation, prediction) but can be harder to train. RNNs are good for long-term dependencies but can be computationally slower. Deep Learning Software Tools and Implementation  TensorFlow, PyTorch, Keras, and others offer powerful tools to build and train DL models. They are often free and constantly updated by a large community. Online tutorials and documentation make learning DL more accessible. TensorFlow Mature framework, easy to use, especially with the Keras open-source library that provides a Python interface for artificial neural networks). It has a large community and is production-ready, with good visualization tools. However, it may have less of a "Python-like" feel in its basic form and there may be potential compatibility issues between versions. PyTorch Feels like standard Python coding, easy to install and debug, offers more granular control of the model. However, without Keras, it requires more coding effort and the performance can vary depending on how you customize it. We have a team of BI analysts who tailor solutions to fit your organization's unique requirements. They create sharp dashboards and reports, leveraging advanced statistical and machine learning techniques to uncover valuable insights from complex healthcare data. Contact our experts to integrate BI for a comprehensive view of patient care, operations, and finances.
Alexander Suhov • 19 min read
Gamification in Healthcare: the Value of Fun
Gamification in Healthcare: the Value of Fun
What is gamification Gamification implies integrating game mechanics and design techniques into non-game experiences. This process motivates audiences participation and engagement while making mundane tasks more fun and interactive. ‘If you want somebody to do something, go to the next screen, or get them to physically go to a place, use the location services, have them check-in, and give a happy little exploding confetti reward for that on the phone, and you’d be shocked at how effective that is.’ Amanda Havard, Health: ELT CEO For more information on gamification as a whole and the mechanisms behind its effectiveness, see our article. In the context of health IT, gamification is typically employed in medication adherence, medical education-related simulations, fitness and wellness apps. The strategy is to use rewards for users who complete mandated tasks, and typically works in the following ways: By filling a progress bar to measure success. Thus, developers invoke progress-related instinct. ‘An estimated 50% of patients with chronic diseases do not follow the prescribed treatment. Gamified health tracking creates an environment that keeps the patient from straying from the appropriate therapy path.’ Dr. Bertalan Meskó, Director at the Medical Futurist Institute By allowing users to share progress and results with their friends/other players or designing an “honor roll”. Thus, developers create a competitive spirit to stimulating the use of the service. Fitness App. Reword Unlock by Olha Hurenko Source:dribbble.com/shots/4492657-Fitness-App-Award-Unlocked By awarding points, medals, stars, achievement badges or giving virtual currency during each stage of progress. Thus, developers create a sense of accomplishment and increasing motivation levels. Kenko Health Avatar by Yoann Baunach Source: dribbble.com/shots/4288089-Kenko-The-avatar-of-your-health In specialized health apps targeted to older users, individuals with movement or sensory impairments, gamification experiences are created using real-time biofeedback from motion-capture sensors and gesture-control technology. ‘Games don't need to be complex. We tapped into dance as a form of engagement.’ Dr. Doug Elwood, Executive Health and Wellness Leader An often overlooked benefit of gamified healthcare applications is their potential for gathering relevant patient data. Software like this motivates users to give more feedback which, in turn, helps companies find trends, make products that address the needs of the target audience better, and even create new business models. However, due to legal restrictions in countries like Germany and France, gathering data requires the attention of the corporate lawyers, as well as developers. Moreover, it presents an ethical and a cybersecurity challenge.  Gamified healthcare is a big deal: one report predicts the market for it to reach 4.2 billion dollars by 2022. Another one forecasts it to grow to a whopping 13.5 billion dollars by 2025. Look how gamification techniques can be used in e-learning projects. Or get help to implement game elements in your app. Gamifying healthcare: case studies The move to gamification of healthcare, however, seems to be a welcome one. According to PwC’s Top Health Industry Issues of 2017 report, 78% of respondents aged between 25 and 44 said they would use some form of gamification in their treatment. What software healthcare solutions are more relevant? Fitness and nutrition apps Self-management chronic condition and medication apps Healthcare apps for kids Physical therapy and rehabilitation apps Emotional health apps Motivating Wheelchair-bound Patients to Exercise Many people suffering from spinal trauma or dysfunction and having to use a wheelchair are also at risk of cardiovascular diseases. Exercise is difficult for them and the fact that they are sitting for most of the time only exacerbates the situation. Daily activities don’t help maintain the necessary level of activity. Fortunately, there is GameWheel - an interface that allows connecting wheelchair to the computer as a controller in specialized games. According to a study by scientists from several American universities, it proved effective in both motivating the patients to exercise more and in making the periods of exercise more productive. The study participants used GameWheel to play a racing game where pushing their physical push on the wheel translated to the speed of the car on the screen. As a result, the heart rate, oxygen consumption, and ventilation were higher in the players than in their non-playing counterparts. Moreover, some reported that they were so absorbed in the game, that they forgot they were exercising. Helping Cancer Patients The “Re-Mission” game series has proven to be effective in helping children and young adults suffering from various forms of cancer. Re-Mission: Nanobot’s Revenge Not taking their medicines on time is a widespread problem in patients - up to 50% of them either fail to take the drugs regularly or don’t file the prescription at all. In the case of life-threatening diseases, it becomes extra important to solve. That’s why a non-profit HopeLab Foundation has commissioned a serious game to address the issue. “Re-Mission” put the player in control of the “Roxy” nanobot that was to fight cancer with chemoblaster, radiation gun, antibiotic rocket, and other weapons derived from actual medical treatments. It proved to be a huge success, so “Re-Mission 2”, a suite of free-to-play online games was launched. These games improved the treatment adherence rates, and also increased the patients’ self-efficacy - confidence that the disease can be defeated. Fitness and nutrition apps Apple’s 2014 App Store review of 100+ health apps proved a direct correlation between gamification elements embed and high user ratings. MyFitnessPal used the highest number of gamification techniques. We all know Fitbit as one of the early innovators in the wearables game. However, the company is positioning itself as the go-to device for employers. Fitbit has almost 1.500 corporate wellness program customers including BP, IBM, and Bank of America. Most of them give their employees Fitbit devices to track their workout progress and health habits. Source: play.google.com/store/apps/details?id=com.fitbit.FitbitMobile Fitbit is an example of how corporate partners are becoming more involved in mHealth apps. Such tactics, therefore, allows employers to reduce employee healthcare costs by improving lifestyles or providing instant access to non-emergency care. However, one of the most striking examples of how companies accepting healthcare gamification is Apple. They award Apple Watch and iPhone users with badges for accomplishing workout tasks like hiking and cycling or surpassing daily totals like calories burden. Source: macworld.co.uk/how-to/apple/apple-watch-activity-achievement-badge-3658788 Awarding badges is part of Apple’s continued push into healthcare. The Health app on iPhone, Workout app on Apple Watch and Activity apps on both have distinct functions but can define user’s health status in details. The strategy helped Apple boost Apple Watch sales and own 2017 wearables market. Source: imore.com/apple-watch-and-activity-tracking-what-you-need-know Unlocking wellness achievements turns into a naturally popular behavior. The wild success of Pokémon Go demonstrated how willingly people play achievement-oriented games simply for the fun of earning points. Importantly, Pokémon Go proved that game playing is not always about passive experience - players are required to walk around and keep moving. ‘A lot of fitness apps come with a lot of "baggage" that end up making you feel like "a failed Olympic athlete" when you're just trying to get fit. Pokémon Go" is designed to get you up and moving by promising you Pokémon as rewards, rather than placing pressure on you.’ John Hanke, Pokémon Go CEO Yet Pokémon Go does get people moving more but the effect doesn’t last. In fact, the market offers more than 9.000 healthcare-related apps. Many of them are downloaded, used once or twice, then forgotten. The exercise-tracking startup Pact pursues a highly-motivating policy among people looking to improve their health. Users make pacts promising to exercise or eat healthier. Source: play.google.com/store/apps/details?id=com.gympact.android&hl=en By failing to meet their target, users have to pay a monetary penalty between $5 and $50, while those who succeed get a part of the payment. Smartphone location data and photos taken in gym serve as the evidence. Players can specify how much they would be fined if they failed to meet a pact. This money then goes to a collective pool that allocates payouts among those who do reach their goals. The powerful driving force of this mHealth app is that users can actually lose money when they fall off track. Unfortunately, even when players held up their part of the pact, the company allegedly failed to provide the funds promised. Pact must return about $1M as of September 2017. Self-management chronic condition and medication apps By helping patients understand their chronic conditions better and by simplifying medication management using gamification, patient compliance rates can be increased to achieve better outcomes. Gamification techniques can make the tedious and repetitive tasks of treating a chronic illness rewarding and more engaging. Diabetes is considered the “the disease of the 21st century”. The 2016 study revealed that many experts support the idea of creating an enjoyable experience for patients living with this chronic disease. ‘Naturally people like to be rewarded. Thus, if this [gamification] is applied to the self-management of diabetes, it would be very effective. [...] it will change the view and the experience of self-management of diabetes for the patient.’ from Gamifying Self-Management of Chronic Illness: A Mixed-Methods Study ‘Positive reward is enjoyable in whichever form it comes. This will help patients’ self-esteem.’ from Gamifying Self-Management of Chronic Illness: A Mixed-Methods Study Indeed, having diabetes requires self-management skills vital to prevent the complications associated with the disease and maintaining the healthy life. Gamified apps can help patients self-manage in a more efficient and entertaining manner. They also give them the opportunity to be appreciated for their efforts and to positively compete with one another. Now, what gamification elements are most commonly used? Tracking measures of blood glucose, insulin, food intake, and other related info. Getting feedback based on the entries. Being notified when blood glucose measures fluctuate. Glooko provides a remote patient monitoring platform for diabetes that enables users to connect any glucose meter, insulin pump or CGM. The company also offers a FDA-approved app to help patients manage their care and control outcomes. Source: play.google.com/store/apps/details?id=com.glooko.logbook mySugr is an another example of a gamified solution for diabetes management. The company is remarkable that they developed a separate app for children (mySugr Junior app). Mango Health mobile app reminds patients when it time to take their medicines and records each dose. It also automatically warns users about dangerous interactions between drugs and supplements or with food and drink. By taking medications properly, patients earn points to be redeemable for gift cards or charitable donations. Source: play.google.com/store/apps/details?id=com.mangohealth.mango&hl=en Unfortunately, gamification cannot diminish the seriousness of diabetes or any other chronic disease. Patients need help not only to enhance their illness self-management but also to be understood and supported by other victims. Gamified healthcare apps for kids Younger users usually do not understand the importance of long-term therapies or medications, regarding their illnesses as short-run miseries. They do not want to swallow bitter pills or have shots, do not want to be in therapy or stay in the hospital. Gamification can help children forget they undergoing medical treatment, teach them responsibility for their health. Inspired by Minecraft, Pfizer (a pharmaceutical company) launched a video game aimed at educating younger hemophilia patients, aged 8 to 16, about the importance of adhering to their treatment plans. Hemocratf is a simulated environment where players interact with the “village doctor” to learn how their treatments work. Kids are challenged to monitor factor levels and self-infuse to help control bleeding if needed. ‘These new digital innovations can be integrated into everyday routines to help empower people with hemophilia to learn about and track different aspects relevant to their disease so that they can have informed conversations with their healthcare providers.’ Dr. Kevin W. Williams, CMO of Pfizer Rare Disease, said Zamzee (acquired by Welltock) developed an activity tracker and rewards system for children to get them moving and complete quests based on their physical level. In a randomized controlled study, kids using Zamzee were nearly 60% more active. Young players collect points by moving and completing challenges. Earned points can be exchanged for virtual rewards, like equipment for their on-site avatars, or physical rewards, like pink duct tape sent to their address. Physical therapy and rehabilitation apps After a serious injury, it is difficult and time-consuming to reach even an agreeable level of independence regarding movement or other activity. Gamification takes a chance to reimagine the physical therapy experience. Reflexion Health offers a patient-facing telerehabilitation solution known as VERA. This platform controls the movements of patients practicing physical therapy exercises. The system works in patients’ homes allowing them to watch an animated instructor model on TV or PC. Motion tracking technology compares patients’ performance with the sample and gives guidance and correction suggestions if needed. As VERA helps patients recover function over time, it is essential to encourage, measure and report patient engagement and objective performance of their progress. ‘This focus on developing an ongoing relationship with specific patients, along with the framework it requires and the metrics it produces, are major differences from broadly-released, “fire-and-forget” games for health.’ Mark Barrett, Lead Software Engineer at Reflexion Health The GlassOff program is developed to eliminate dependency on reading glasses by enhancing users brain’s image processing function. The recovery process consists of several sessions that are mini visual recognition games. Working through GlassOff exercises takes about 12 minutes. It’s recommended to follow the program 3 times a week for 3 months. The app automatically reminds users when it’s time for the next session. Source: play.google.com/store/apps/details?id=com.glassesoff.android Emotional health apps Happify toolset helps users improve their emotional well-being, overcome stress and anxiety that have a negative impact on daily life. Their app has 30+ tracks to choose from and tracks user progress to see how their skills compare. Each track is based on scientific research from neuroscientists and psychologists at Harvard, Stanford or Penn. ‘After six to eight weeks, 86% of users who use the program for the recommended time and dosage come back and say they feel happier and much better.’ Ofer Leidner, Co-Founder of Happify Source: play.google.com/store/apps/details?id=com.happify.happifyinc&hl=en Looking for an attractive healthcare business model? Find your inspiration in our articles: Top 20 healthcare SaaS companies from New York How to design a healthcare app Top healthcare mobile apps using React Native How can gamification help your business Employers, insurers, and healthcare providers are focusing more energy on keeping people out of the hospital by helping them manage their own health. Thus, the market demands high-quality and complex solutions that make getting healthy more fun. Gamification delivers proven and tangible results. By applying gamification elements into the product, businesses have experienced an increase in engagement across social media and website traffic generated. To bear all the valuable fruits of gamification, businesses have to understand the environment to which it is applied. In other words, specific gamification techniques need to be tailored and adapted by this specific audiences. Already in favor of gamification in your healthcare app? Fill our online “get a quote” form to start.
Alex Shestel • 9 min read

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