Clinical staff will work around a process for big data for patient outcomes that obstructs care, and they will be right to. Accreditation bodies assess patient outcomes on documented evidence of consistent practice. It is appropriate for those preparing to take on wider responsibility for this aspect of health and pharmaceutical services. Applied research in health and pharmaceutical services consistently shows that early structure around big data for patient outcomes reduces downstream rework. It treats the health and pharmaceutical services discipline as an operating discipline and equips participants to run it as one. Participants gain a clear basis for measuring what patient outcomes has actually achieved. Cases are chosen to expose the trade-offs in big data for patient outcomes rather than to illustrate ideal conditions. The version of patient outcomes described in the manual and the version practised on the floor tend to diverge over time. The programme closes with an action plan for this aspect of health and pharmaceutical services that each participant writes for their own organisation.
Expected Learning Outcomes
✓
Build a register of the risks attaching to big data for patient outcomes and keep it current.
✓
Build the quality indicators for patient outcomes that reflect patient outcome, not activity.
✓
Design escalation for clinical deterioration detected through big data for patient outcomes.
✓
Review contracts and agreements for the obligations they create around patient outcomes.
✓
Build informed consent and ethics practice into big data for patient outcomes.
✓
Control cost per episode within patient outcomes without shifting risk to patients.
✓
Integrate big data for patient outcomes into existing management routines rather than running it separately.
Who Should Attend
✓
Quality and patient safety officers monitoring big data for patient outcomes.
✓
Operations staff who encounter the consequences of patient outcomes directly.
✓
Medical directors overseeing clinical governance of big data for patient outcomes.
✓
Infection prevention and control practitioners covering patient outcomes.
✓
Newly appointed managers taking on big data for patient outcomes for the first time.
✓
Pharmacists and pharmacy managers involved in patient outcomes.
Course Modules
Open any module to see its sessions and topics.
01
Big data for patient outcomes: quality indicators and patient outcome
2 sessions · 8 points
Session 1Matching staffing on big data for patient outcomes to real demand
Write down the assumptions underpinning the approach to big data for patient outcomes.
Check that consent processes within patient outcomes are genuinely informed.
Reduce unwarranted variation in big data for patient outcomes between shifts and sites.
Define escalation criteria for deterioration detected through patient outcomes.
Session 2Building the method for patient outcomes step by step
Define acceptance criteria for big data for patient outcomes in advance.
Confirm patient data handled within patient outcomes meets confidentiality obligations.
Assign responsibility for keeping documentation of big data for patient outcomes current.
Close out actions on patient outcomes rather than leaving them open indefinitely.
02
Patient outcomes: infection prevention and patient safety
2 sessions · 8 points
Session 1Investigating an adverse event involving patient outcomes
Assemble the accreditation evidence pack for big data for patient outcomes.
Apply structured root cause analysis to the last adverse event involving patient outcomes.
Cost big data for patient outcomes per episode and identify avoidable waste.
Confirm the incident reporting route for patient outcomes is quick and non-punitive.
Session 2Who answers for patient outcomes, and to whom
Reduce the variation in how big data for patient outcomes is carried out between teams.
Map the handovers in patient outcomes between functions and secure them.
Set escalation thresholds for big data for patient outcomes that work out of hours.
Build medication safety checks into patient outcomes at the point of highest risk.
03
Patient outcomes: medication and treatment safety
2 sessions · 8 points
Session 1Consent, confidentiality and ethics in patient outcomes
Plan continuity of big data for patient outcomes during surge and outbreak conditions.
Record what was learned when patient outcomes did not go as planned.
Identify infection transmission risks created by big data for patient outcomes and control them.
Rehearse the briefing on patient outcomes that would follow an incident.
Session 2Testing big data for patient outcomes before relying on it
Set out the decisions in big data for patient outcomes that require sign-off and by whom.
Check that patient outcomes still works when volumes rise unexpectedly.
Verify cold chain and storage conditions for materials used in big data for patient outcomes.
Confirm that reporting on patient outcomes reaches the people who can act.
04
Patient outcomes: incident reporting, investigation and learning
2 sessions · 8 points
Session 1Designing patient outcomes so it helps rather than obstructs care
Build the competence framework that supports big data for patient outcomes.
Confirm licensing and regulatory requirements applying to patient outcomes are met.
Map the clinical workflow for big data for patient outcomes as it is actually performed.
Close the loop: confirm changes to patient outcomes improved the indicator they targeted.
