‘Knowledge’ in the NHS –
some points for discussion
Claudia Pagliari
The power of knowledge
• “Knowledge is the enemy of disease”
Potential to prevent or minimise:
• unknowing variation in clinical practice & service delivery;
• errors of commission & omission;
• waste;
• failure to implement new knowledge & technology systematically &
appropriately;
• over-use and under-use - inappropriate care;
• unsatisfactory patient experience;
• poor quality clinical practice;
• failure to manage uncertainty or ignorance.
From the NKS website
• "Knowledge management is a
conscious strategy for moving the
right knowledge to the right people
at the right time to assist sharing
and enabling the information to be
translated into action to improve
the organizational performance."
(O'Dell & Grayson 1997)
Knowledge vs. information
• ‘Data’ & ‘Information’
via interpretation/analysis/integration
Challenges for quality &
accessibility
Challenges for transformation
• > Knowledge
• Acquired knowledge (passive)
• Applied knowledge (active)
Barriers to ‘knowing’ (e.g. learning
models/media, accessibility)
Challenge of translating
knowledge to behaviour (e.g.
needs opportunity, immediacy,
incentive etc.)
Knowledge Management: The Core Processes
Knowledge
Application
Knowledge
Asset Management
Knowledge
Discovery
In practice, what forms of
knowledge are of most relevance
to different stakeholders?
What are the challenges?
Clinical practitioners
•
Professionalism and development
– Knowledge about current best practice: e.g. NLH, evidence synthesis (e.g. BMJ
Clinical evidence), clinical guidelines (e.g. SIGN)
• Challenges: getting current and relevant evidence to users at an appropriate
time and in an appropriate format; in a way that fits with work practice
•
Patient care
– Electronic health records & analysis (individual- and population-level)
• Challenges: Effective data integration (quality, coding, interoperability, security);
effective communication (e.g. data displays) and transformation (e.g. for
personalised care plans or risk assessment)
– Clinical decision support (e.g. aiding evidence-based prescribing; treatment;
prevention; diagnosis; intercepting & flagging errors and risks)
• Challenges: e.g. Scaling up systems; avoiding over-rides; understanding use in
practice; maintaining currency
•
Quality improvement
– Local/national/practice-based performance statistics
• Challenges: Accurate and fair interpretation (mindful of context effects); ‘target
culture’ and competing/perverse incentives (e.g. focus on paid indicators)
Integrated records
CDS
• Classic decision support links patient data
with knowledge sources to inform and
facilitate actions (e.g. guideline-compliant
prescribing or referral; personalised risk
assessments)
• But can also integrate monitoring, error
checking and, through learning functions,
can contribute to knowledge discovery ->
A Continuum of Clinical Decision Support
and Knowledge Discovery*
„ Surveillance „ Interactive
Reference
Reference
Knowledge
Knowledge
Linking
Linking
Event
Event
Monitoring
Monitoring
„ Monitoring
patient
data with
passive
decision
support
„ Proactive
Safety
SafetyNet
Net Anticipation
Anticipation
„ Intercepting „ Making the
right
incorrect
decisions
clinical
the easiest
decisions
decisions
*modified from the First Consulting Group Model of Clinical Decision Support
„ Learning
Understanding
Understanding
and
andPredicting
Predicting
Performance
Performance
„ Predictive
Modeling
„ Casebased
Reasoning
„ Learning
Knowledge
Repository
The public & patients
•
Online health information
– Information about conditions, treatments, prevention etc.
• Challenges: Raising awareness; quality assessment. Promoting responsible
use; managing accessibility and usability
•
Service comparisons
– Performance & quality stats (transparency & choice agenda)
• Challenges: how to frame these best to avoid inappropriate interpretation
and use
•
Supporting self care
– Decision aids (may integrate personal utilities)
• Challenges: must be intuitive but useful
– Personal Health Records
• Challenges: best modes of sharing; responsibilities for data quality
– Telehealthcare
• Challenges: how to integrate multiple sources of knowledge (patient,
communities, professionals; library sources); how to cope with and use new
data streams; how to integrate systems into organisational practices
Knowledge is power
Cybermedicine
Telemedecine
Industrial age medicine
Ambulatory
medicine
Hospital
medicine
Internet
Prevention+
Self Help
Self
Care
Assisted
Care
Disease
Management
Consumer
Professionals
Eysenbach G: Consumer health informatics. BMJ 2000;320:1713-16
Clinical Medicine / Curative Med
Public Health / Preventive Med
Information age health care
Implications of the empowered
patient
• Positive
– Better health/self-care
– Better communication & partnership with
professionals
– More informed choices
• Negative
– Challenges to traditional roles
– Trust and liability (why can’t I get x?)
NHS Choices
• “a dynamic web-based service using latest
technology to provide an easy- to-use,
‘one-stop shop’ of easily accessible health
and healthcare information for the 21st
century. The information can be
personalised for different patients”
http://www.nhsdirect.nhs.uk/
Tele-monitoring and self-care
Personal eHealth Records
• NHS HealthSpace
– Personal web space for patients, supporting diaries,
appointment & booking information + EHR extracts
(planned)
• Plans to give patients’ access to their EHR via
HealthSpace and other systems
• Potential benefits for patient safety (error
checking), self-care; relationships;
• Challenges – data security, reliability, privacy
•
Policymakers
– Using scientific knowledge
• Challenges: suitability of trials and systematic reviews for
policy audience (training issues; relevance to priorities)
– Experiential knowledge
• Challenges: How to aggregate & feedback learning
(avoiding repetition and mistakes)
– Tracking innovation
• Challenges: grey sources; marketing hype (how to judge
‘the next big thing’ when there is no formal evidence yet)
– Surveillance stats
• Challenges: appropriate usage (dangers of over- or wronginterpretation or extrapolation)
Researchers
• Keeping up with the science
– E.g. Journals; scientific databases; Medline; trial registers; NLH,
Cochrane etc
• Challenges: poor indexing, cumbersome searches
• Doing data-driven research
– E.g. Linkage of disease registers and patient records for
epidemiology; pharmacovigilance; family studies; genomics
research etc.
• Challenges: accessing data; evaluating quality; integrating
sources; managing consent, privacy and governance
Some generic considerations
• Tailoring knowledge types and architectures to
context and group (e.g. localised vs national
repositories; managing accessibility & usability)
• Understanding sociotechnical & ethical issues
– Training & organisational change management
– Managing sensitive data
– Coping with new roles & responsibilities in the
knowledge era
• organisations; professionals; patients
– Valuing and acting on patient ‘knowledge’
– Using experiential learning and tacit knowledge in the
EBM era