PART 1 - Purpose and Underlying Technology
Clinical Decision Support Systems (CDSS) were first introduced in the 1980s and have since experienced a significant increase in adoption and effectiveness within healthcare. This growth has been fueled, in part, by endorsements from US government acts, such as the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which allocated $22.6 billion to incentivize the implementation of health information and clinical decision support systems. These efforts aimed to promote the use of electronic health records and its associated capabilities and additional tools to improve care coordination, quality of care, and reduce health disparities. As a result, the CDSS market has experienced remarkable growth, with an estimated market value of approximately $2.1 billion USD. Experts predict that the market will continue to expand, with a projected value of $3.81 billion by 2030, resulting in a CAGR of 6.83%. These figures are a testament to the increasing importance of CDSS in healthcare, as they offer healthcare professionals timely, evidence-based recommendations and real-time access to relevant patient data, ultimately leading to improved patient outcomes.
Though CDSS can be classified as a mature and saturated market, Spinnaker has conducted extensive research on CDSS over the past several months, exploring the market landscape, emerging CDSS startups, the applications of CDSS, and industry trends. This series of posts, which we plan to release over the coming months, will provide a commentary on CDSS and their evolving role in healthcare. In this initial post, we introduce CDSS and provide an overview of their purpose and underlying technology.
Clinical Decision Support Systems
Clinical Decision Support Systems (CDSS) are computer-based tools that provide healthcare professionals with actionable information and medical knowledge to aid in clinical decision making. They are designed to help clinicians make better informed decisions about patient care by providing them with real-time information and latest evidence-based recommendations. These software systems can take many forms ranging from simple reminders and alerts to complex algorithms and predictive models. Today, CDSS are often integrated into electronic health records (EHRs), computerized clinical workflows, or any similar healthcare information system to enable clinicians with easy access to patient data and clinical guidelines.
CDSS are built to improve the quality of care, increase patient safety, and enhance efficiency in healthcare delivery. To achieve this, any CDSS created will adhere to the ‘five rights’ principle that lays the foundation to any successful CDSS framework:
“delivering the RIGHT information to the RIGHT person in the RIGHT intervention format through the RIGHT channel at the RIGHT time”
By following the ‘five rights’ CDSS can provide clinicians with timely, relevant information and recommendations that can help improve screening and early detection, diagnosis, and treatment and patient outcomes.
Technology
From a technological standpoint, a typical CDSS contains three core elements: a base or data management layer, an inference engine or processing layer, and a user interface layer. The data management layer consists of patient data, clinical databases that stores information on diseases, diagnoses and medications, and clinical pathway systems in the form if-then decision trees (knowledge-based) or machine learning models (non-knowledge-based). The inference engine or processing layer serves to incorporate all elements of the data management layer by applying rules and algorithms from the knowledge or non-knowledge-based system to the clinical database whilst simultaneously taking into consideration patient data. The interface layer displays the recommendations and guidance synthesized from the inference engine – this layer can take the form of an EHR system dashboard or a mobile/web-based application.
CDSS can be broadly classified into two categories based on the type of technology the system is grounded in: knowledge-based systems and non-knowledge-based systems.
Knowledge-based CDSS are based on a set of rules and algorithms that are derived from expert medical knowledge and clinical guidelines. These rules can be made using literature-based, practice-based, or patient-directed evidence. Fundamentally, knowledge-based systems rely on formalized knowledge representations, such as ontologies, decision trees, and if-then rules to provide clinicians with recommendations and alerts based on a patient’s medical history and data on current condition.
Non-knowledge-based CDSS still requires a data source, but further relies on an additional layer of artificial intelligence, machine learning algorithms and statistical models to make predictions or recommendations. Techniques that are often incorporated in non-knowledge-based CDSS to identify patterns and correlations in patient data include data mining, clustering, neural networks, and genetic algorithms. Further, depending on the application and intended use of the CDSS, non-knowledge-based CDSS can also incorporate natural language processing and image analysis to extract meaningful information from unstructured data sources such as clinical notes or medical images.
Knowledge-Based CDSS
Advantages:
Grounded in evidence-based medical knowledge due to formalized knowledge representations such as ontologies, decision trees, and if-then statements.
Easy to maintain and update as new medical knowledge becomes available.
Highly customizable to meet specific needs of healthcare providers and organizations.
Highly consistent which can reduce variability in clinical decision making.
Disadvantages:
Limited scalability as the number of rules and knowledge sources grows.
Time-consuming to develop due to significant reliance on input from clinical experts and knowledge engineers.
Limited to known knowledge which may not be comprehensive or up to date.
Difficulty in capturing tacit knowledge – knowledge that is difficult to express or codify may not be effectively captured.
Non-Knowledge-Based CDSS
Advantages:
Ability to handle and analyze complex large datasets.
Highly adaptable to changes in patient data and adjust recommendations accordingly.
Machine learning capabilities allow the system to continuously learn and improve over time, leading to more accurate and personalized decision support.
Disadvantages:
Lack of interpretability – CDSS that uses deep learning can be difficult to interpret and understand making it challenging to validate recommendations or troubleshoot issues.
Limited knowledge transfer if a system is trained on specific datasets – difficult in transferring knowledge to new datasets or patient population.
Reliance on data quality – systems rely on completeness and quality of data to train the system which can lead to poor or inaccurate recommendations.
Both knowledge-based and non-knowledge-based CDSS come with their advantages and disadvantages; therefore, in recent years, there has been an increasing shift in interest for the development of hybrid CDSS. These systems aim to leverage the strengths of both approaches by integrating formalized knowledge representations with machine learning and statistical models. Creating a hybrid CDSS allows clinicians access to the precision and transparency of knowledge-based systems with the flexibility and scalability of non-knowledge-based systems.
Moving on, the applications of CDSS and their use cases will be explored in the next post of this series.
Sources:
Castillo, Ranielle S, and Arapad Kelemen. “Considerations for a Successful Clinical Decision Support System.” NursingCenter, https://www.nursingcenter.com/lnc/ce_articleprint?an=00024665-201307000-00003.
Straits Research. “Clinical Decision Support Systems (CDSS) Market Size Is Projected to Reach USD 3.81 Billion by 2030, Growing at a CAGR of 6.83%: Straits Research.” GlobeNewswire News Room, Straits Research, 26 July 2022, https://www.globenewswire.com/en/news-release/2022/07/26/2486236/0/en/Clinical-Decision-Support-Systems-CDSS-Market-Size-is-projected-to-reach-USD-3-81-Billion-by-2030-growing-at-a-CAGR-of-6-83-Straits-Research.html.
Sutton, R.T., Pincock, D., Baumgart, D.C. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit. Med. 3, 17 (2020). https://doi.org/10.1038/s41746-020-0221-y
Admin. “Introduction to Clinical Decision Support System (CDSS).” Omics Tutorials, 12 Aug. 2021, https://omicstutorials.com/introduction-to-clinical-decision-support-system-cdss/.
PART 2 - Applications of Clinical Decision Support Systems
Clinical Decision Support Systems (CDSS) are an essential tool for healthcare professionals in their daily decision-making processes. CDSS provides clinicians with real-time, evidence-based recommendations that help improve the quality of care, increase patient safety, reduce healthcare-related costs, and enhance healthcare efficiency. With the rise of electronic health records (EHRs) and healthcare information systems, CDSS are becoming increasingly integrated into clinical workflows, enabling healthcare professionals to access patient data and clinical guidelines seamlessly.
In this post, the various applications of CDSS and their impact on healthcare delivery will be explored. For this analysis, the applications of CDSS have been categorized into six segments based on clinical point of care along the healthcare spectrum: Preventative Care, Diagnosis, Planning or Implementing Treatment, Follow Up Management, Hospital and Provider Efficiency, and Cost Reductions and Improved Patient Convenience.
Preventative Care
CDSS can play a vital role in preventative care by enabling healthcare professionals to identify patients who are at possible risk of developing certain conditions and by providing them with appropriate interventions early. Through leveraging personal health records, CDSS can screen patient data such as medical history, medications, and demographic information and conduct genomic interpretations and predictive analytics that are specifically geared towards the patient, thus conducting a thorough risk assessment of the patient prior to any potential onset of disease. The advancement of electronic health records and its ability to easily integrate third party software has enabled CDSS to gain access to such data.
Diagnosis
CDSS have significant potential to support healthcare professionals in the diagnostic process. Similar to CDSS, Diagnostic Decision Support Systems (DDSS) aims to guide healthcare professionals through the diagnostic procedure ensuring that all potential diagnosis is considered. Throughout the process, elements such as patient health records and test results are all factored in for the DDSS to process and analyze in hopes of outputting probable diagnoses, or at a minimum guide healthcare professional with potential steps moving forward.
Analyzing laboratory test results and radiology images are perhaps two of the most common applications of DDSS. Through leveraging non-knowledge based DDSS for radiology test results, AI and ML algorithms can be used to accurately detect diseases ranging from Alzheimer’s disease to dermatological conditions. Similarly, the rise in enhanced imaging and precision radiology (radiomics) has the potential to revolutionize cancer care through providing clinicians with more detailed information about the characteristics of tumors and how they are likely to respond to different treatments. The process of extracting and analyzing quantitative features of a radiographic image allows the characterization of tumor biology, treatment response, and clinical outcomes. Through leveraging ML algorithms to analyze large amounts of data from medical images, radiomics is capable of identifying patterns and associations that may not be visible to the human eye.
Likewise, through EHR integration, DDSS can access patient-specific laboratory and pathology test results and apply predictive algorithms to help diagnose conditions or suggest further testing. Analyzing these test results in a deeper and more complex manner may avoid the need for patients to pursue more invasive diagnostic procedures. On a surface level, DDSS can identify and interpret abnormal test results and suggest further testing or diagnoses based on the patient’s medical history and other clinical factors.
Planning and Implementing Treatment
CDSS technology can play an integral role in treatment planning and implementation. Such systems can provide clinicians with real-time, evidence-based recommendations for treatment plans, which can help improve overall health outcomes and reduce the risk of errors. There is currently a wide range of different CDSS platforms on the market that are specialized towards provider-specific targeted tools, care-setting specific tools, and disease specific tools. However, there are two underlying applications where CDSS are most commonly used: clinical management adherence and drug selection.
CDSS can be used to monitor and aid a healthcare provider’s treatment and clinical practice guidelines and ensure a level of care quality that patients deserve. One of the most beneficial features of CDSS is that it adds an element of personalization. Such systems will take into consideration medical history and patient preferences into the equation thus generating a treatment plan and establishing a care pathway that is unique to the patient, thus increasing the likelihood of improved outcomes. Furthermore, CDSS can be adopted by hospitals to ensure that providers remain adherent to hospital policies, protocols, and operating procedures. Simultaneously, providers would be able to have continued access to the latest medical knowledge and discoveries to ensure that their patients are receiving the most up-to-date treatment.
Another common application of CDSS is its use within the Drug Selection process. Leveraging its access to EHRs and personal medical history, these systems can consider many factors that can be missed by patients and doctors such as potential drug allergies, ideal drug dosage and frequency, potential of duplicate therapy in the presence of a common active ingredient, and harmful drug to drug or drug to gene interactions. By allowing CDSS to run an additional layer of analysis will prevent any further downstream complications and help plan and implement a patient’s treatment.
Follow Up Management
Use of CDSS in follow up management allows healthcare providers with the necessary information and reminders to ensure timely and appropriate follow-up care for patients. For instance, CDSS can be used to alert clinicians when a patient is due for a follow-up appointment or a diagnostic test. As a result, an element of automation is established in the administrative and back-end work of a healthcare setting. However, the recent rise of remote patient monitoring solutions has potential ties to CDSS. Through leveraging remote patient monitoring technology, healthcare providers would have continued access to patient health data in real-time, enabling them to detect potential health issues and intervene appropriately. All of this data would be collected and stored in a patient’s EHR, and because CDSS are integrated into EHRs, CDSS can help monitor patients for potential adverse events related to their treatment and provide recommendations for any necessary follow-up actions.
Hospital and Provider Efficiency
Navigating the healthcare landscape is a complex and timely process, and while not an absolute solution to the problem, CDSS can play an important role in improving hospital operations and provider efficiency. By improving the accuracy and speed of diagnosis, CDSS can help reduce the time required to diagnose and treat patients, leading to improved patient outcomes and provider efficiency, whilst simultaneously allowing for a smoother healthcare experience for all stakeholders involved. CDSS can also help streamline clinical workflows by providing real-time information and decision support. This can help providers and hospitals prioritize tasks, optimize resource utilization, and reduce administrative and operation burden. Care coordination is also an aspect of healthcare services that can be vastly improved through the implementation of CDSS. Through careful treatment planning, patients and primary care providers can anticipate potential referrals of other providers. Combined with the ability to provide access to patient information across different care settings and necessary healthcare stakeholders, this can help reduce duplication of services, improve communication between providers, and ultimately improve health outcomes and patient experience, all whilst minimizing cost.
Cost Reduction and Improved Patient Convenience
As previously stated, CDSS can help healthcare providers make more accurate diagnoses and treatment decisions, which can lead to fewer medical errors, fewer complications, and shorter hospital stays. Additionally, the cost of medications and other treatments may fall as CDSS may have the capability of identifying generic alternatives and cheaper options that are equally as effective. As a result, CDSS can help healthcare providers deliver high-quality care at a lower cost, which can be beneficial to both patients and healthcare organizations.
CDSS can also improve patient convenience by providing more accessible, convenient, and personalized care. Patients can use CDSS tools such as telemedicine platforms, chatbots, and mobile apps to communicate with their healthcare providers, schedule appointments, renew prescriptions, and access their health records. This can help reduce the need for in-person visits, which can be time-consuming and expensive, and provide patients with more flexibility in how they receive care. Moreover, CDSS can help patients better manage chronic conditions by providing personalized recommendations, reminders, and alerts. This can help patients stay on top of their health and avoid unnecessary hospitalizations or emergency room visits.
Conclusion
CDSS have emerged as invaluable tools in modern healthcare. Their applications are far-reaching, that has encompassed the entire clinical point of care on the healthcare service spectrum. As technology continues to advance, the potential for CDSS to revolutionize healthcare delivery and improve patient experiences is boundless.
Our next post of this CDSS series will explore the benefits and drawbacks of CDSS. Stay tuned!
PART 3 - Benefits and Drawbacks of Using CDSS
Clinical Decision Support Systems (CDSS) have emerged as powerful tools in the healthcare domain, revolutionizing the way healthcare providers make decisions and improving patient outcomes. By leveraging advanced algorithms and data analysis, CDSS offers numerous benefits that enhance the quality, efficiency, and safety of healthcare delivery. However, like any technological solution, CDSS also comes with its own set of challenges and drawbacks. In this post, we will explore the benefits and drawbacks of using CDSS in healthcare. From improving diagnostic accuracy and reducing medical errors to enhancing patient convenience, CDSS holds great potential in transforming healthcare practices. Nevertheless, considerations such as interoperability, bias and fairness, and limited clinical reasoning must also be carefully examined. By delving into the advantages and limitations of CDSS we can gain a comprehensive understanding of its role in shaping the future of healthcare.
Advantages
Improved Patient Outcomes: CDSS can help healthcare professionals make more informed and accurate decisions about patient care, which can lead to improved patient outcomes.
Reduced Medical Errors: Through the consideration of a holistic approach including potential downstream implications, CDSSs can help reduce the risk of medical errors.
Personalized Care: CDSSs can provide personalized recommendations and information that is tailored to the patient’s needs and condition.
Compliance with Guidelines: CDSSs can assist healthcare professionals in providing care that is consistent with current guidelines and standards.
Cost Savings: CDSSs can help healthcare organizations save money by reducing medical errors, uneccesary tests and procedures, and by improving the efficiency of care.
Access to the latest evidence-based practices: CDSSs can provide patients with access to the latest evidence-based practices and guidelines, which can ensure that patients receive the best possible care.
Better use of Resources: CDSSs can help healthcare organizations make more efficient use of resources by identifying and managing high-risk populations and by reducing unnecessary tests and procedures.
Disadvantages:
Data Quality and Availability: CDSSs rely on accurate and complete patient data, which may not always be available or of sufficient quality. Inaccurate or missing data can lead to incorrect or incomplete information being provided to healthcare professionals.
Interoperability: CDSSs often need to connect to other systems, such as EHRs, which can be a challenge if the systems are not interoperable.
User Adoption: CDSSs can be complex and difficult to use, which can lead to resistance from healthcare professionals to adopt and consistently use the system.
Bias and Fairness: CDSSs can perpetuate bias and discrimination if the data used to train the model is biased or if the system is not fair for certain groups of patients.
Limited Clinical Reasoning: CDSSs can provide information and guidance, but they cannot replace clinical reasoning and expertise of healthcare professionals.
Limited Support for Certain Cases and Specialty: CDSSs may not be able to provide support for rare or unusual cases, which can limit their usefulness in certain situations.
Overreliance on the System: Overreliance on the system can lead to healthcare professionals becoming less proficient in certain areas and may lead to a lack of critical thinking when the system is not available or not providing a suitable recommendation.
CDSSs offer numerous advantages that have the potential to revolutionize healthcare practices and improve patient outcomes. However, it is important to acknowledge the challenges and limitations associated with CDSS implementation. In essence, CDSSs should be developed and implemented with a comprehensive understanding of both the advantages and limitations to avoid potential pitfalls and ensure their successful integration into healthcare workflows.
CDSSs hold great promise in transforming healthcare delivery by improving decision-making processes, patient outcomes, and resource utilization. By addressing the associated challenges and limitations, CDSSs can truly revolutionize the future of healthcare, offering valuable support to healthcare professionals while respecting the importance of their clinical expertise.
PART 4 - Looking Into the Future of Clinical Decision Support Systems
As technology continues to advance, the future outlook for Clinical Decision Support Systems (CDSS) in the healthcare domain holds immense promise and potential. These systems, leveraging cutting-edge technologies such as AI, ML, Big Data, and Real-Time Analytics, are poised to continue to solidify their role in supporting healthcare professionals’ decision-making process.
The Clinical Decision Support System market is projected to grow at a CAGR of 11.2% from 2024 to 2030 (1). Concurrently, as more hospitals and healthcare providers begin to adopt and implement CDSS into their everyday workflow expectations surrounding patient outcomes, diagnostic accuracy, and healthcare efficiency are all projected to improve. As governments and health ministries continue to endorse the utilization of CDSS, there is a growing consensus of the need to construct a formalized self-learning knowledge base that is accessible for all key stakeholders (2). This ensures that recommendations are aligned between different systems, and that fundamentally there are minimal discrepancies between what the latest and most accurate medical practices and treatment plans are.
However, predicting trends, future directions, and the evolution of any market is like attempting to navigate without a map, as uncertainties and unforeseen variables can greatly influence the final outcome. Therefore, it is worth looking at how CDSS and its underlying purpose can extend its benefits into emerging industries and other applications. To help with this, let’s look deeper into the conversation Vivek Patkar had with HealthcareTransformers.3 Vivek Patkar is the Chief Medical Officer and Co-Founder of Deontics Ltd, a medtech company in the field of AI and Clinical Decision Support.
During Vivek’s discussion with HealthcareTransformers five emerging trends in healthcare were identified where CDSS will play a huge role moving forward: data-driven patient management, learning healthcare systems, value-based healthcare, remote or virtual monitoring, and sensor technology.
Data-driven patient management: The rise in Big Data in today’s day and age has simultaneously created a rising tide in real world medical data. Coupled with advancements in AI and Machine Learning to create innovative healthcare products and services, this shift towards data-driven care is needed more than ever. At a high-level, CDSS may not seem like the obvious catalyst to such development and evolution; however, an important component of CDSS holds the answer. Fundamentally, every CDSS contains a knowledge base from where medical knowledge and patient-related information are extracted before processed. It is the compilation of data in this knowledge base that will help yield healthcare from evidence-based medicine to data-driven care.
Learning healthcare systems: The rise of generative AI has the potential to allow for CDSS to review and monitor its own recommendations for future refinement. The ability of AI to reflect on its own outputs can construct and refine treatment pathways and clinical guidelines to ensure maximum effectiveness and efficiency.
Value-based healthcare: The rise of value-based healthcare care models will need to rely on a set of guidelines that yields the greatest care effectiveness in the most efficient and cost-effective manner. CDSS has the potential to provide recommendations that would not only streamline healthcare services but would also improve patient outcomes whilst minimizing costs.
Remote or virtual monitoring: The rise of remote and virtual monitoring of patients plays an important role in relieving the burden placed on healthcare systems. By managing less complex patients through a virtual medium, hospital resources are reserved for those of higher priorities. CDSS can play an important role here in determining the severity of patients, provide clinical guidelines for virtual or remote care, and to a certain extent automate the process with some degree of oversight.
Sensor technology: Stemming from the rising trend of remote and virtual monitoring, medical devices specifically designed to monitor patients and collect data is simultaneously gaining traction. CDSS will benefit greatly from the amount of data collected by these devices and turning them into recommendable action items to help patients and providers.
While the future of CDSS is promising and its theoretical application is sensical, we believe that the key to the future success of CDSS and its potential importance in a variety of different healthcare mediums lies within the software’s user interface and user experience, and integration into the current clinical and administrative workflow. Convincing providers and key healthcare stakeholders to meaningfully utilize CDSS and leverage its capabilities to its fullest extent will truly unlock the potential of CDSS, as opposed to seeing it as another administrative barrier that is required by laws and regulations.
Clinical Decision Support System Industry Report
In recent years, the remarkable surge in the prominence of AI has revolutionized countless industries. With its ability to process vast volumes of data, recognize patterns, and make informed decisions, AI has reshaped the way society has approached problem-solving and innovation. In the same manner, healthcare - an industry traditionally defined by its risk aversion and conservative uptake of technology, has seen transformative influence by the presence of AI technology. However, in a field that is still dominated by human expertise, experience and decision-making, the rise in Clinical Decision Support Systems (CDSSs) represents a pivotal shift, as it introduces a powerful technology-driven resource that is capable of enhancing and, in some cases, even automating critical healthcare decisions.
CDSSs were first introduced in the 1980s and have since experienced a significant increase in adoption and effectiveness within the US healthcare system. This growth has been fueled, in part, by endorsements from the US government, primarily through the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. This initiative played a pivotal role in fostering the growth of CDSSs by incentivizing healthcare providers to adopt EHRs and related Health Information Technology Systems, which in turn created a robust foundation and ecosystem for the integration and utilization of CDSSs in clinical workflows. As a result, today, CDSSs represents a $2.1B market value, with a projection of $3.81B by 2030, growing at a CAGR of 6.83%. Estimates also reveal that in 2013, 41% of US Hospitals with an EHR system also has a CDSS functionality adjacent to it, with the percentage increasing to 90% in hospitals and 80% in clinics in 2017.
With very high adoption rates, it comes to no surprise that the CDSS market is a relatively saturated market with an even blend of large corporations to early-stage startups all involved in the space fighting for a slice of the market. Large established entities in the space include:
Siemens Healthineers
Siemens Healthineers has a number of products in the CDSS space such as the AI RAD Companion which is an AI powered cloud app to help with analyzing CT, X-Ray and MRI scans of the brain, prostate, chest, and other organs, the Prisca System, which is a CDSS for prenatal risk calculation based on biochemical markers, ultrasound measurement, and patient medical history, and the Medicalis, which is a cloud-based CDSS mechanism to ensure compliance with the Appropriate Use Criteria program. However, most notably, their Protis Data Management System and Protis Assessment Software is their most comprehensive CDSS. The data management system consolidates a patient’s test result from different platforms into a single graphical report, whilst the assessment software helps physician interpret clinical data.
Merative (formerly IBM Watson Health)
In 2022 IBM divested and spun-off their Watson Health division to form Merative which was acquired by Francisco Partners, a Private Equity firm based in San Francisco. Merative’s CDSS platform called Micromedex has seven core capabilities to offer providers and clinicians everything needed to make informed decisions – these capabilities include: core drug references, expanded drug references, neonatal and pediatrics content, toxicology, drug supplier and pricing data, formulary content, and patient education content. Furthermore, through a partnership with DynaMed, DynaMedex was created to seamlessly combine disease and condition information from DynaMed, and drug and interaction insights from Micromedex to create an all-in-one solution for optimal provider experience.
While several established leaders dominate the CDSS landscape, Spinnaker has identified several emerging startups with the potential to introduce innovative solutions that address unmet needs and gaps within this already saturated and competitive space.
Glass Health
Glass Health is a Seed Stage Startup that provides physicians and clinicians access to AI-assisted diagnosis and clinical decision-making suggestions via their differential diagnoses and clinical plan drafting software. Equipped with an in-house evidence-based, peer-reviewed, clinical guideline community library, Glass Health creates a closed CDSS network that aims to increase diagnostic accuracy, improve patient outcomes, and eliminate clinician burnout. Further, a simple UI/UX platform makes Glass Health’s CDSS system easy to use.
Curbside Health
Curbside Health is a Seed Stage Startup that aims to align administrative priorities with clinical actions that drive outcomes through an end-to-end clinical effectiveness solution. As a result, Curbside Health has developed tools for medical organizations to build and customize their own clinical practice guideline, manage and maintain their clinical guidelines, implement the guideline into their respective EHR Systems, and extract Analytical Insights on the performance usage of the implemented clinical guidelines.
Clearstep Health
Clearstep is a Seed Stage Startup that provides clinical-grade AI assistant dedicated towards patients that helps them navigate the patient journey as well as optimize care team efficiency on the provider side. The company has three products available: a virtual triage symptom checker, a patient engagement chatbot, and a patient care navigation software. Together, these solutions provide patients with the ability to accurately self-navigate to the right care and services needed, whilst also providing physicians greater insights and efficiency with the built-in clinical AI workflow automation.
AvoMD
AvoMD is a Seed Stage Startup that digitizes and optimizes the latest clinical evidence for point-of-care usage and adoption. AvoMD allows medical organizations to ultimately increase the usage of clinical pathways and other critical information by allowing physicians and clinicians to use and access medical information at the bedside through their point-of-care app and clinical pathway builder (AvoBuilder).
CDSSs have become indispensable tools in today’s healthcare setting, offering real-time, data-driven insights that empower physicians and clinicians to make informed decisions. These systems, by leveraging advanced technology and vast data repositories, enhance diagnoses, treatment plans, and overall patient care. Their ability to analyze complex data, predict outcomes, and provide tailored recommendations significantly boosts efficiency, reduces errors, and ultimately leads to improved patient outcomes. It is evident that CDSSs have come a long way, but the need to embrace and advance these systems aligns with the continually evolving healthcare landscape, one that is defined by enhanced precision, quality, and efficiency.
Author: Shawn Tjahaja
If you are interested in learning more, get in touch at strategy@spinnakerLS.com.
Spinnaker offers true partnership and comprehensive guidance to help leaders navigate the complexities of the Life Sciences industry and chart a path to success. From early-stage market assessment through commercial execution and ongoing lifecycle management, we deliver tailored solutions to ensure optimized practicable results.
References:
Straits Research. “Clinical Decision Support Systems (CDSS) Market Size Is Projected to Reach USD 3.81 Billion by 2030, Growing at a CAGR of 6.83%: Straits Research.” GlobeNewswire News Room, Straits Research, 26 July 2022, https://www.globenewswire.com/en/news-release/2022/07/26/2486236/0/en/Clinical-Decision-Support-Systems-CDSS-Market-Size-is-projected-to-reach-USD-3-81-Billion-by-2030-growing-at-a-CAGR-of-6-83-Straits-Research.html.
Siemens Healthineers - https://www.siemens-healthineers.com/en-us
Merative - https://www.merative.com/
Glass Health - https://glass.health/
Curbside Health - https://www.curbsidehealth.online/
Clearstep Health - https://www.clearstep.health/
AvoMD - https://www.avomd.io/