Predictive analytics uses a variety of data, statistical algorithms, and machine learning to precisely and comprehensively foresee risks delivering the recommendations to improve outcomes.
Predictive Analytics empowers doctors, nurses, patients, caregivers and others with appropriate recommendations.
Jvion uses predictive analytics to identify patient's risk across various diseases and clinical events. AI then recommends appropriate action for each patient – taking into account clinical, socioeconomic and behavioral data — in addition to clinically-validated best practices. Armed with this intelligence, healthcare organizations can improve quality, cost, and the overall patient experience.
Here is the guidance to help you develop business and a high-value use case for Predictive Analytics 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: Predictive analytics 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.
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: 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 for incorporating Predictive Analytics is a challenge and requires specialist input from various care professionals.
Multidisciplinary team: We need an interdisciplinary team consisting of computer scientists, patients, nurses, caregivers and clinicians to align goals, requirements and clinical trial outcomes.
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 requirements 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 'Predictive Analytics'?
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 Predictive Analytics 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?
https://jvion.com/