Clinical Decision Support System

Clinical Decision Support Use Case Development Guidance

Clinical decision support system (CDSS) assists healthcare professionals by converting real-time medical-related data, documents, and knowledge based into a set of sophisticated algorithms, applying techniques such as machine learning, knowledge graphs, natural language processing, and computer vision to help healthcare providers improve diagnosis, treatment, and prognosis. 

Key benefits

CDSS empowers doctors, nurses, patients, caregivers, pharmacists and others to make more informed decisions to deliver effective care.

  • Diagnostic support
  • Informed decision making
  • Medication therapy
  • Actionable insights using real-time data and analytics
  • Better self-management and early identification of adverse events
  • Improved information acquisition on treatment and diagnosis

Use Case Examples

There are many AI-powered CDSS that serve as a guide to healthcare professionals.

• Analyse lifestyle, patient history, clinical and laboratory data to identify patients at risk of cardiovascular disease.

• Analyse patient clinical data to determine which over-the-counter side-effect, counter-interactions and allergy symptoms.

• Analyse heart rhythms to detect atrial fibrillation and other abnormalities and alert caregivers and clinicians, whenever appropriate.

• Google Brain AI CDSS analyzes images on the back of the eye to diagnose diabetic retinopathy and diabetic macular edema, which is the leading cause of blindness. 

• The clinical decision support resource shows impact in reducing medical errors, the third-highest cause of U.S. deaths (Wolters Kluwer Health)

Use case development template

Here is the guidance to help you develop business and a high-value use case for clinical decision support systems using Artificial Intelligence and Machine Learning.

This CDSS use case framework guidance describes Esdha's current research on the topic and should be viewed only as recommendations, unless specific regulatory or statutory requirements are cited.

Challenges & Opportunities

Data: CDSS relies on centralised, clinical data and real-time data sources leading to lack of inadequate supplies in hospital and real-time clinical decision support to healthcare professionals. For example, a study has shown that when pneumococcal vaccine inventories run out, it is not updated in CDSS.

Operational Impact: Poor data quality can affect the quality of decision support provided.  There is a need for information standards such as ICD, SNOMED, and other sources.

Transportability and interoperability: with the diversity of of clinical data sources,  system exists as stand-alone imposing greater challenges to implementation. Cloud infrastructure helps to reduce the interoperability issues. 

System monitoring & maintenance: CDSS rely on real-time data and knowledge base.  Healthcare institutions have reported difficulty in monitoring and maintaining the knowledge base, algorithms, rules and data. 

Lack validity and human decision making: as users can become more reliant on CDSS without questioning the accuracy of the recommendation provided. 

Disruptive alerts: CDSS alerts patients and healthcare professionals using alerts. Studies have found that up to 95% of alerts are inconsequential, leading to fatigue from alerts or distrust. For example, alert on duplicate medication for inflammatory bowel disease can be found inappropriate as the same drug can be applied through different administration routes for treatment. 

Knowledge base: overall knowledge creation with the clear evidence base for incorporating CDSS is a challenge and requires specialist input from various care professionals.

Interdisciplinary team: We need an interdisciplinary team consisting of computer scientists, patients, nurses, caregivers and clinicians to align goals, requirements and clinical trial outcomes.

Accountability: CDSS gives rise to structures in which agency is shared - used by different healthcare professionals (nurses, pharmacists, clinicians, physicians) with reliance on CDSS for mutually intertwined and interdependent decisions. This rises the question as to 'who is accountable or morally and legally answerable' to adverse outcomes. There is a need for frameworks on medical malpractice liability for AI CDSS.

Cost: due to lack of standardised metrics, it is hard to do cost benefit assessment as cost-effectiveness depends on a range of socio-economic factors including environment, political and technological.

Potential risks & mitigation

Trustworthiness: different stakeholders have distinctive expectations which needs adequate risk-benefit analysis for building rules and outcome measures.

might not be required or can be misleading as users may not follow CDSS recommendation. There is also risks associated with misunderstanding recommendations or wrongfully assume causality as explanations are correlation-based, they can be susceptible to error due to random factors. Explainability is useful as long as the outputs are sufficiently accurate, validated and required by the user.
Wrong or misleading recommendation: can result in loss of trust or serious consequences. 

Privacy & quality: adherence to data protection and privacy requirements26 27 such as the general data protection regulation (GDPR) will be essential. A standardised approach to data collection can help to address this risk.

Bias, overfitting and validity: build a rigorous criterion to evaluate for biases (such as statistical misrepresentation to the general population), overfitting, and validity.

Key questions for business and use case development

Here are some of the questions to consider for business and use case development.

Understanding stakeholders

What do you think about 'CDSS'?
What are your biggest challenges?
How do you think we can address the challenges?
Are there any barriers?
What are the regulations & legal requirements?
What are their expectations & intention of use?
Any conflict of interest?

Clinical Discovery & Process

What patient problem you are trying to address?
Current decision making process? Who is involved in the treatment?
What difference would CDSS achieve?
Are there any adverse consequences?
What the the different systems used?

Data Source & ICT Systems

Do you have access to the required data sources?
Is there a standardised approach for data collection?
How is the quality of data?
What is the current infrastructure?
What systems would you need access to?
Are there any restrictions?

Problem & Value Determination

What is the problem?
Is there a need to solve the problem?
What is the scope, boundaries & context?
Analysis of socio-technical scenarios
Would patient outcome be effective using CDSS? 
Cost-benefit and risk-benefit analysis?

Regulation, Bias. Privacy, Ethics & Safety

How would you safeguard privacy & comply with law? 
Would misuse of data/ algorithm contribute to social/ ethical problems?
Map to trustworthy AI
Risks, ethical tensions & mitigations
What patient groups can be denied opportunities/ face negative consequences?

Capability, High-level Architecture & Data Pipeline

Do you have a multidisciplinary team?
Do you have access to AI experts for the project?
Do you have support from the executives, clinicians, patients, regulators & others?
Do you have a systems view of the architecture and data pipeline?

Data Source Integration & infrastructure

Do you have access to data?
How will your existing systems integrate?
What computing & data storage power do you need?
How will you monitor KPIs?
What is the infrastructure?
Any dependencies/ issues?
What would be the harm in providing the solution?
Data maintenance process

AI Strategy, Commercial Strategy & Costs

What is your value proposition?
Is your AI strategy aligned with the business strategy?
What are the future prospects & commercial viability?
Do you have the required finance for the project?
Does the financial forecast cover ongoing maintenance? 


An overview of clinical decision support systems: benefits, risks, and strategies for success

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

How Artificial Intelligence-Powered Tools Can Support Clinical Decision-Making

Primer on an ethics of AI-based decision support systems in the clinic

Paper: How to use the EU’s Trustworthy AI Guidelines in Practice

New Japan study finds clinicians make significantly fewer mistakes when using UpToDate from Wolters Kluwer

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