How we can help on using AI in healthcare service delivery. We have a lifetime of experience engaging with clinical stakeholders such as doctors, nurses, allied health professionals, senior management and boards. We can support patient-focused AI initiatives as well as ways to include community support agencies in working with or adopting ML technologies.
Assess which Machine Learning applications align with your organisation’s strategic priorities, operational goals, and resource capabilities and quality outcomes objectives.
Assist in identifying feasible and high-impact ML applications including KPIs operational performance and PROs, etc.
Integrating ML into quality improvement initiatives and clinical governance frameworks.
Develop strategic roadmaps for the adoption, pilot testing, and scaling of ML solutions, including ROI and risk management.
Assess organisational ‘maturity’ and readiness to adopt ML technologies and related solutions.
Help assess regulatory considerations and address ethical concerns.
Advise on how ML tools can be integrated into existing clinical workflows including workflow redesign and patient journey optimisation. Recognize operational inefficiencies, patient care challenges, and quality improvement areas where ML could provide solutions.
Some technical areas you may find of relevance
Proficiency in the types of ML models most applicable to healthcare, such as predictive modelling, natural language processing, and deep learning (LSTM, transformers).
Familiarity with how ML is used for clinical decision support, patient risk stratification, operational efficiency, resource allocation, and personalized medicine.
Knowledge of healthcare data sources (EHRs, wearables, claims data) and integration methods.
Skills in validating ML models for healthcare applications, ensuring models are interpretable for clinical users.
Technology Assessment to help client establish specifications and select ML platforms, tools, and vendors.
Some of the tools in our tool kit
| LEAN analysis | Forecasting and scenario development; signal indicators etc. |
| Value Stream Mapping | Gated models |
| Goldratt Constraints | Decision architectures (psychological models) |
| Blue Ocean strategy | Soft Systems Methods |
| Complex challenges, e.g. Morphological analysis | Logic models |
| Socio-technical Systems Analysis |