Belitsoft > Healthcare Software Development > Clinical Decision Support Software

Modern Clinical Decision Support Software: AI-Based and Analytics-Rich

Modern clinical decision support software speeds up the documentation and adds quality, immediately showing the value. According to some forecast reports, the clinical decision support software market is expected to grow significantly (CAGR of more than 15%). North America dominates this market. It's an opportunity for healthtech startups. For example, the AI-based clinical insights platform Regard startup raised $76.3M in funding, while Etiometry has got $24.6M for its analytics-rich critical care decision-support platform. This type of software integrates patient data and then provides insights for diagnosis and treatment.

Contents

Diagnostic Challenges in Clinical Medicine

Vital diagnoses are often being spotted too late or overlooked due to copious documentation and clunky EHRs, medications are not accurate as a frequent consequence of the mistakes like accidentally discontinued medications or double-dosing, and additional revenue opportunities are missed caused by incomplete, nonspecific documentation.

Often, such information is so critically important that it can change the course of management.

Missing Significant Diagnoses or Medication Interactions

The volume of stored healthcare data has skyrocketed, and with future integrations like Apple Watches and Fitbits, it will continue to grow. More data is better—if you can use it. Otherwise, the sheer volume of EHR data simply crowds out pertinent PHI. It’s physically impossible for a physician to review all the data in a patient’s medical record thoroughly. Clinicians are now able to act on only 3% of vital information, while other data—including potential diagnoses or medication interactions—is buried within digital records, leaving 97% of patient data in the EHR untapped in clinical decision-making.

Patient safety may be compromised by missed critical patient information or diagnoses at the point of care.

This is especially critical for hospitalists, who often see patients at their sickest for the first time but must make life-saving decisions without a deep understanding of past treatments and conditions.

Hidden critical insights for making diagnoses, informing treatment pathways, and ultimately bolstering revenue remain inaccessible.

Revenue Cycle Teams Struggling

The absence of full medical documentation at the point of care complicates proper billing and contributes to claim denials.

Revenue cycle teams have to query doctors, preventing physicians from focusing their energy on patient care and instead forcing them to spend time on administrative work. Workflows to review diagnoses in the discharge summary and validate Diagnosis-Related Groups (DRGs) assignment to ensure revenue integrity are often retrospective - days or even weeks after the patient left the hospital.

Documentation re-review also take time and contribute to burnout. Physicians hate to sit in front of the computer and document. The job that used to be fulfilling and rewarding became burdened with administrative tasks, insurance paperwork, and prior authorizations.

Clinical decision support software shrinks clinicians’ blind spots. AI tools for real-time clinical documentation move forward the healthcare system, which is struggling with expensive care delivery, reducing profit margins, and ongoing staffing shortages.

Clinical Decision Support Software Features

AI-Powered Diagnosis Suggestions

Modern clinical decision support software leverages AI for chart reviews and generative AI for notes and discharge summaries.

Such platforms may be integrated into clinicians’ existing workflow, operate in the background, and deliver insights directly into the EHR, acting as a clinical co-pilot and preventing clinicians from toggling between screens.

They review the entire patient chart, including 100% of available patient data, as well as data from other digital health tools integrated into the EHR (HIE, lab, and pharmacy data), processes it through a series of algorithms, and then flag missed diagnoses and returns a summary in a way that doctors understand, formatted as a medical note. It alerts physicians to safety issues like gastrointestinal (GI) bleed patients who are still on aspirin.

The software synthesizes large amounts of patient data from electronic health records, analyzes structured and unstructured data, and suggests diagnoses, many of which may be Complications/Comorbidities (CCs) and Major Complications/Comorbidities (MCCs), helping validate Severity of Illness (SOI) and Risk of Mortality (ROM) more accurately.

The software also links to supporting documentation and formats a progress note with relevant details for evaluation. It curates all information into a single view with hyperlinks to the original sources.

The platform can generate a list of differential diagnoses that are consistent with the patient data to help explore distinct possibilities for treatment plans. Symptoms like weight loss, fatigue, and night sweats may indicate diabetes, a thyroid disorder, lymphoma, or tuberculosis. Abnormal blood counts and enlarged lymph nodes may be signs of leukemia, lymphoma, myeloma, or an infection. A patient with a skin lesion may have melanoma, squamous cell carcinoma, basal cell carcinoma, or seborrheic keratosis. If, in addition to current symptoms, there is data in the patient's history supporting certain diagnoses, it will definitely be used.

EHR Integration

Modern clinical decision support software is fully integrated into the EHR, especially the largest ones. For example, it is available on Epic and Cerner’s app stores, which allow third-party integrations via APIs, making integration smooth. It can appear as a link in the EHR’s menu or as a tab that doctors can click on, with an option for a split-screen view.

After a doctor opens this tab or clicks on the link, the software processes patient data and presents the results without requiring them to leave the EHR. It can generate the assessment and plan, assist doctors in diagnosing by providing relevant labs, imaging, and prior history data—eliminating the need to search through records manually. It also identifies new diagnoses based on patterns in the data and labels them as “AI-detected conditions,” allowing doctors to review them before adding them to their notes.

“This is what I imagined EHRs would be like.” “It makes me a better doctor.” Such reviews are not common among key users.

AI Models for Medical Diagnosis

Models for such tools are trained on data from multiple health systems, and a team of physicians reviews outputs during training before they go live.

ChatGPT and similar models can summarize text, but they aren’t designed for medical diagnosis. OpenAI prohibits the use of its models for diagnosis because of the risks of hallucinations. However, their generative AI is a good option for non-diagnostic tasks, like writing discharge summaries.

Benefits of Enterprise-Wide Adoption

Such software may be installed in outpatient clinics and hospitals, ranging from single-site facilities with a few hundred beds to large systems with dozens of hospitals.

Integrated healthcare delivery systems with operating revenues in the tens of billions, serving millions of patients with the help of thousands of providers, report that they deploy the technology enterprise-wide after several months of pilot programs with a high voluntary adoption rate because it really helps physicians identify missed CCs and MCCs, highlight for clinical attention important diagnoses they would have likely never recognized, and even identify paused medications that had never been resumed—now causing complications for reasons clinicians would not have found on their own.

Better captured diagnoses impact reimbursement. This includes CCs and MCCs that drive DRGs as well as Hierarchical Condition Categories (HCCs) that affect risk-adjusted payments. Health systems that automate risk adjustment see annual reimbursement increases of millions of dollars.

How Belitsoft can help

Belitsoft provides platform engineers and engineering operations teams to help you build and maintain clinical insights platform’s fast and with high quality, aligned with your engineering goals. They will enhance your software functionality, performance, and availability. With experience in startup and high-growth environments, health technology, and enterprise SaaS products, they can scale your platform’s data and integration capabilities, support analytics, BI, and customer data integration with EMR/EHRs, and assist with security audits (HIPAA, SOC 2, etc.).

Hire our senior data engineers to create your data platform from the ground up, establish data warehousing architecture, build source data models for analytics, BI, and data projects, modify data warehouse schemas and ETL tooling, integrate BI platforms (Tableau, Looker, etc.) into the analytics data warehouse, and implement monitoring and alerting systems. They are experienced in SQL, data engineering programming languages like Python, and AWS tooling (Glue, Kinesis, DMS, etc.).

Our experienced senior UX/UI designers will create intuitive internal and enterprise client-facing dashboards, data visualization user interfaces, and admin tools, making it easier for users to navigate and interpret complex data. In turn, our senior frontend software engineers will build frontend applications using modern web development frameworks (primarily React & TypeScript, but not limited to them) and tooling for modern web applications (Webpack, Node.js, npm/Yarn, Jest, etc.). They are skilled in debugging and diagnosing complex frontend issues, working with end-to-end testing frameworks (Cypress, Selenium, etc.), addressing frontend performance and observability challenges, and driving projects forward.

Involve our DevOps engineers in all stages of custom clinical decision support software development. They will help you enhance and support cloud-based architecture and infrastructure across the product platform, maintain containerized environments (Kubernetes, EKS, Docker, ArgoCD, etc.) and core CI/CD infrastructure, integrate your software product infrastructure into customer environments and electronic medical record systems, write infrastructure as code (Terraform, CloudFormation, Pulumi, etc), and, in general, participate in idea generation and planning, design and prototyping, building, and maintenance of your systems.

Our senior support engineers are comfortable reviewing logs, databases, and the codebase of automated diagnosis tools that streamlines clinical and revenue cycle efforts to identify potential issues. They use Datadog, Sentry, AWS services, etc., or internal support tools for root cause analysis and fault isolation, understand APIs, and have the skills to write SQL to diagnose issues. They are able to operate in dynamic startup environments and can work on mature enterprise SaaS products involving patient health data and large health systems.

We also can help with the ONC Certification by making Clinical Decision Support Software system compliant with the ONC Certification Criteria for Health IT like integrating MFA using Smart on FHIR and building EHI export features (single-patient and population, CSV format, public documentation link, admin restrictions) and more.

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Delivery Manager
"I've been leading projects and managing teams with core expertise in ERP development, CRM development, SaaS development in HealthTech, FinTech and other domains for 15 years."
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