Helped clinical solutions provider with a Medical Record Documentation Platform using MS Dotnet
Software Engineering services helped a leading clinical documentation and document sharing solutions provider to develop a Medical Record Documentation Platform Key Business Outcomes 99% Eased Doctor Dictation Tasks 99% Accuracy in Medical Record Documentation Seamless Integrations with EHRs in Hospitals and Out-patient Clinics About Customer Customer offers clinical documentation and document sharing solutions. They provide clinical information and electronic health record templates. They serve customers throughout the United States. Customer Challenge The customer felt a need for an error-free medical record documentation for doctors and clinicians. Their existing system struggled to integrate comprehensive data from various healthcare facilities leading to fragmented patient records and increased administrative burden on doctors. The customer sought a robust solution to streamline the doctor’s clinical documentation workflow and improve the accuracy and quality of patient records and other patient-related information.Solution Delivered Customer partnered with Adroitent to build a state-of-the-art medical record documentation platform to assist doctors across its outpatient clinics, hospitals, and ambulatory surgery centers to efficiently complete medical record documentation of patients. This platform plays a vital part in the daily clinical workflow for its users due to its essential functionality and integration points into an organization’s clinical systems. The medical record documentation platform translates doctors’ dictations into text through advanced backend editing, ensuring precise patient documentation. Additionally, it offers rhythm jobs for post-dictation editing, allowing doctors to correct errors, if any, directly on their devices (iOS and Android) and submit them to the Electronic Health Record (EHR) system.Solution Highlights Architecture and design: Crafted a scalable and secure architecture to support front end mobile interface with backend processing on web. Database design, architecture, and implementation: A robust database system was developed to handle complex data securely. Comprehensive data integration: Seamless integration of existing system data with the new system was done. Quality assurance: Total quality assurance implemented in end-to-end workflow and data validation and verification to ensure solution reliability. Build, release, and deploy: DevOps CI/CD implemented to build and release iterative cycles for timely deployment of the solution. Key performance metrics: Real-time analytics was provided to monitor and improve doctor’s documentation tasks. Support and maintenance of project: Post implementation, the Adroitent team provided technical enhancement and support to ensure optimal performance. Technology Stack The technology stack used was .Net Framework, MVC, WCF, Web API, and XMPP. The database used was in-app Swift 3.0/4.0 for iOS apps and SQL Server. The data sources used were REST API, XML, and CSVs. The tools used were QuickBox and Zendesk. Key Features Developed Mobile app syncing: Dictations were supported on both Android and iOS mobiles where the doctor had the patient check-in details referred as a job which was synced to the backend database. The doctor could pick the job, and record the patient health conditions and processes. Secure messaging: The doctor can chat with the doctors/nurses in the clinic with the inbuilt Secure Messaging feature. Seamless integrations: The solution was made to fit seamlessly into existing physician workflow patterns due to seamless integrations with APIs ensuring no loss in productivity. Information routing: Automatic routing of clinical information with full interfaces eased front-end scheduling along with back-end clinical systems. Work list for doctors: A full interface was developed into the clinic scheduling system for the doctors, providing them the look and feel of an EHR. Doctor’s identity: Providers were not required to identify themselves or the patient, significantly reducing dictation time and clinical errors. Voice recognition: Dictation was automatically sent to data centers for the processing and simple voice recognition created the first clinical draft. Rhythm jobs: Doctors could edit any dictation errors in the device itself and submit them to the EHR system. If they felt too many edits were required, then they could submit for transcription to editors to further edit them. Rhythm jobs were made available for editing after dictation submission. Once edited, the finished work was routed back to the clinic or hospital for automatic delivery to their EHR or data repository. Integration with HL7: The platform was seamlessly integrated with HL7. Business Outcome Saved Doctor’s time : The platform significantly reduced the time doctors spent on paperwork allowing them to focus more on patient care with streamlined clinical documentation. It helped to ease doctors’ dictation tasks by 99% Accelerated EMR adoption: Helped improve physician satisfaction with accelerated EMR adoption Improved documentation accuracy: Advanced backend editing and post-dictation editing features reduced errors and improved the quality of patient records. It helped to ease doctors’ dictation tasks by 99% and ensured high accuracy in medical record documentation. Improved coordination and eased patient care: Unified and complete patient records with seamless data integration across healthcare facilities improved coordination and eased patient care. Easy adoption for doctors: The platform’s in-depth design and architecture resulted in a user-friendly interface, and made it easy for doctors to adapt and efficiently use the system. Enhanced operational efficiency: Intensive quality assurance and robust architecture ensured the platform’s scalability and reliability, building trust among users and enhanced the overall operational efficiency. Connect with us [wpforms id=”2376″ title=”false” description=”false”]
