Use Case for Predictive Analytics in Healthcare Responsible AI Governance Strategy Guidance

Responsible AI governance use case framework to help you develop business strategy for Predictive Analytics
Predictive Analytics in healthcare - responsible AI governance strategy

Predictive analytics uses a variety of data, statistical algorithms, and machine learning to precisely and comprehensively foresee risks delivering the recommendations to improve outcomes.

Key benefits

Predictive Analytics empowers doctors, nurses, patients, caregivers and others with appropriate recommendations.

  • Disease prevention
  • Disease detection and diagnostics
  • Early detection through symptom analysis
  • Digital patient copies
  • Actionable insights using data and analytics

Real-World Use Case Examples

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.

Responsible AI Strategy Framework

Here is the responsible AI governance use case framework to help you develop business strategy for Predictive Analytics using Artificial Intelligence and Machine Learning.

This responsible AI governance use case framework 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: 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.

Potential risks & mitigation

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.


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