Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the healthcare industry. As there are a lot of such startups working on various different applications, we want to share our insights with you. Here, we take a look at 5 promising clinical decision support tools.
Heat Map: 5 Top Clinical Decision Support Tools
For our 5 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 650 relevant solutions. Depending on your specific needs, your top picks might look entirely different.
RAMPmedical – Alerts & Reminders
Notifications and reminders are commonly associated with clinical decision support tools. They follow the clinician’s actions, prescriptions and recommended procedures and notify the user via a pop-up alert. Warnings, reminders or notifications appear if the doctor prescribes medicines that cannot be taken together, if a patient has an allergy to some components, or if a drug is not recommended during pregnancy, etc. While important in many situations, this type of clinical decision support tool should be used sparingly, as there’s a high risk of “alert fatigue”.
The German startup RAMPmedical builds a therapy decision support platform enabling doctors to take the optimal therapy decision for each patient among varieties of provided treatment schemes. The software analyzes the chosen treatment guidelines, presents the results to doctors, and provide them with the necessary information about potential risks and conflicts.
Medical Algorithms Company – Documentation Forms & Templates
Utilizing document templates as clinical decision support tools ensures that appropriate data is collected and recorded. Well-designed templates allow clinicians to enter the required data and important additional information in order to get a full picture of the patient’s condition including symptoms, complaints, mood, etc., which is especially important in multidisciplinary cases. Additionally, templates enable clinicians to clearly and accurately record documentation details to be processed by algorithm-based programs, that analyze the patterns and learn from them.
The UK-based Medical Algorithms Company creates Medal, a decision support tool for healthcare professionals with evidence-based medical analytics to improve clinical practice and health outcomes. The system works based on more than 20.000 algorithms and logical decision-making tools to provide medical staff with checklists and recommendations, tailored to their different medical specialties. The predictive analytics platform aims to ensure precise and errorless documentation for medical diagnostics, treatment, and monitoring to make patient assessment accurate and reliable.
Cohesic – Guided Clinical Workflows
This type of clinical decision support tool provides aid for clinical decision-making in multi-step care plans from a long-time care perspective. It provides evidence-based guidelines, recommendations, and pathways at the right time thereby informing about the next steps based on previous results and treatment reactions. This approach is also applicable in situations such as the current Coronavirus pandemic when the medical staff has to follow the same strict guidelines and needs to be informed simultaneously.
Cohesic, a Canadian startup, works on a Care Intelligence Platform that offers data-driven decisions in cardiovascular care via diagnostic workflow. The platform also features structured reporting software and enables diagnostic test providers and clinicians to increase the efficiency of reporting, reduce medical errors and receive greater insights into their patient’s health.
HERA-MI – Diagnostic Decision Support
Clinical decision support systems (DSS) aim at supporting and assisting with clinical decision-making tasks in diagnostics. They help clinicians consider a variety of diagnoses, ask patients more targeted questions, request some of the patient’s data and in response, proposes a set of appropriate diagnoses. Diagnostic decision support tools are integrated with the electronic health record (EHR) and suggest a checklist of symptoms and signs in relation to each suggested diagnosis based on a patient’s history.
The French startup HERA-MI develops a clinical decision support system based on machine learning and medical imaging processing that improve breast cancer early detection. Artificial Intelligence (AI) enables radiologists to decrease the time they spend on non-problematic cases and spend extra time on more complicated ones.
Tapa Healthcare – Condition-Specific Sets
Order sets represent another group of clinical decision support tools that work as a pre-defined template for clinical decisions making for a specific condition or medical procedure. This might be a grouping of orders that help clinicians effectively choose the appropriate items or steps compared to individual orders that improve adherence to evidence-based practices and reduce the risk of errors.
Tapa Healthcare, a startup from Ireland, builds a solution for hospital and community healthcare including automated alerts and clinical decision support features. The Rapid Electronic Assessment Data System (READS) is a bedside clinical assessment tool that provides proactive patient safety by performing quick and easy-to-use mobile assessments, anticipating patient deterioration with validated clinical algorithms, and generating recommended actions designed to improve patient outcomes.
What About The Other 645 Solutions?
While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence for your Proof of Concept (PoC), partnership, or investment targets. The 5 clinical decision support tools showcased above are promising examples out of 650 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.