Reduce avoidable variation in how AI in clinical decision-making is carried out across teams.
Deploying Artificial Intelligence in Diagnosis and Clinical Decision-Making
A working programme in AI in clinical decision-making for managers who have to deliver with existing resources.
📍 Abu Dhabi🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
In healthcare, a weakness in AI in clinical decision-making is eventually paid for by a patient. Variation in clinical decision-making between shifts and sites is where avoidable harm lives. Moving this strand of health and pharmaceutical services from written policy into daily practice is not achieved by a single decision. Post-incident reviews keep identifying weaknesses in AI in clinical decision-making that were visible long before the incident. The programme works equally well for those formalising this area of health and pharmaceutical services for the first time and those improving an existing approach. The result is the confidence to make and defend decisions about clinical decision-making under scrutiny. The programme is built to be used, and every section of AI in clinical decision-making it covers ends in something applicable. The programme uses small-group work so that each participant's treatment of clinical decision-making is examined, not just described. The programme ends where implementation begins, with the health and pharmaceutical services discipline broken into steps someone can start on Monday.
Expected Learning Outcomes
Anticipate the objections that clinical decision-making will attract internally and answer them in advance.
Control cost per episode within AI in clinical decision-making without shifting risk to patients.
Build continuing professional development sustaining competence in clinical decision-making.
Assess the current state of AI in clinical decision-making against a structured set of criteria rather than impressions.
Measure patient experience of clinical decision-making and act on what it shows.
Assess and control infection risk associated with AI in clinical decision-making.
Who Should Attend
Department heads accountable for the results of AI in clinical decision-making.
Medical directors overseeing clinical governance of clinical decision-making.
Hospital and health facility managers accountable for AI in clinical decision-making.
Health information and records staff supporting clinical decision-making.
Quality and patient safety officers monitoring AI in clinical decision-making.
Team leaders and supervisors who put clinical decision-making into practice day to day.
Course Modules
AI in clinical decision-making: quality indicators and patient outcome
2 sessions · 8 pointsSession 1Investigating an adverse event involving AI in clinical decision-making
- Confirm competence and continuing development for staff delivering AI in clinical decision-making.
- Arrange the handover of clinical decision-making so capability survives staff changes.
- Identify infection transmission risks created by AI in clinical decision-making and control them.
- Reduce unwarranted variation in clinical decision-making between shifts and sites.
Session 2What has to be agreed before work on clinical decision-making starts
- Confirm that reporting on AI in clinical decision-making reaches the people who can act.
- Agree the smallest change to clinical decision-making that would be visibly useful.
- Audit AI in clinical decision-making by observing practice, not by reading policy.
- Agree the indicators that will show whether clinical decision-making is improving.
Clinical decision-making: infection prevention and patient safety
2 sessions · 8 pointsSession 1Reviewing clinical decision-making when nothing has gone wrong
- Anticipate the objections AI in clinical decision-making will raise and prepare the answers.
- Rank the weaknesses in clinical decision-making by consequence rather than by ease of fixing.
- Map the clinical workflow for AI in clinical decision-making as it is actually performed.
- Define the trigger that would require clinical decision-making to be redesigned.
Session 2The evidence assessors will ask for on clinical decision-making
- Record what was learned when AI in clinical decision-making did not go as planned.
- Measure patient experience of clinical decision-making and feed it back to the team.
- Define escalation criteria for deterioration detected through AI in clinical decision-making.
- Set escalation thresholds for clinical decision-making that work out of hours.
Clinical decision-making: staffing, capacity and demand
2 sessions · 8 pointsSession 1The paperwork for clinical decision-making that is actually needed
- Compare the cost of AI in clinical decision-making with the cost of its absence.
- Name a single owner for each element of clinical decision-making.
- Set out how exceptions to AI in clinical decision-making are requested and approved.
- Cost clinical decision-making per episode and identify avoidable waste.
Session 2Making incident reporting on AI in clinical decision-making safe for staff
- Map the handovers in AI in clinical decision-making between functions and secure them.
- Apply structured root cause analysis to the last adverse event involving clinical decision-making.
- Close the loop: confirm changes to AI in clinical decision-making improved the indicator they targeted.
- Select quality indicators for clinical decision-making that measure outcome rather than activity.
Clinical decision-making: confidentiality, consent and ethics
2 sessions · 8 pointsSession 1Matching staffing on clinical decision-making to real demand
- Define acceptance criteria for AI in clinical decision-making in advance.
- Check that consent processes within clinical decision-making are genuinely informed.
- Review whether AI in clinical decision-making is aligned with the objectives of the clinical service.
- Benchmark the organisation's clinical decision-making against comparable operations.
Session 2Choosing indicators for clinical decision-making that reflect outcome
- Verify cold chain and storage conditions for materials used in AI in clinical decision-making.
- Plan continuity of clinical decision-making during surge and outbreak conditions.
- Confirm patient data handled within AI in clinical decision-making meets confidentiality obligations.
- Build medication safety checks into clinical decision-making at the point of highest risk.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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