Artificial intelligence and machine learning are successfully being used in the diagnosis and management of treatment.
CDSS empowers doctors, nurses, patients, caregivers, pharmacists and others to make more informed decisions to deliver effective care.
Volpara provides clinically validated, AI-powered software for personalized screening and early detection of breast cancer. Nearly 300,000 new breast cancer cases predicted among US women this year,
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.
Data: AI relies on centralised, clinical data and real-time data sources leading to lack of inadequate supplies in hospital.
Operational Impact: Poor data quality can affect the quality of decision support provided.
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: Healthcare institutions have reported difficulty in monitoring and maintaining the knowledge base, algorithms, rules and data.
Knowledge base: overall knowledge creation with the clear evidence base 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: '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.
Trustworthiness: different stakeholders have distinctive expectations which needs adequate risk-benefit analysis for building rules and outcome measures.
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.
Here are some of the questions to consider for business and use case development.
What do you think about 'medical diagnosis'?
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?
What patient problem you are trying to address?
Current decision making process? Who is involved in the treatment?
What difference would AI achieve?
Are there any adverse consequences?
What the the different systems used?
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?
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 AI?
Cost-benefit and risk-benefit analysis?
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?
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?
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
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?
What are the most important benefits of AI in the healthcare industry? https://neoteric.eu/blog/benefits-of-ai-in-healthcare/